Distinguishing methylation levels in complex biological samples

ABSTRACT

Provided herein is a method for distinguishing an aberrant methylation level for DNA from a first cell type, including steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; and (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score. Also provided herein are sensitive methods for using genomic DNA methylation levels to distinguish cancer cells from normal cells and to classify different cancer types according to their tissues of origin.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 62/401,591, filed Sep. 29, 2016, and U.S. Provisional ApplicationSer. No. 62/268,961, filed Dec. 17, 2015, each of which is incorporatedby reference herein.

SEQUENCE LISTING

This application contains a Sequence Listing electronically submittedvia EFS-Web to the United States Patent and Trademark Office as an ASCIItext file entitled “SequenceListing1396_ST25.txt” having a size of 3kilobytes and created on Jan. 12, 2017. The information contained in theSequence Listing is incorporated by reference herein.

BACKGROUND

The present disclosure relates to determination of methylation patternsin genomic DNA. Specific embodiments relate to prediction, diagnosis,prognosis and monitoring of various conditions based on genomicmethylation patterns.

Changes in cellular genetic information, such as mutations in genesequences which can affect gene expression and/or protein sequence, areassociated with many diseases and conditions. However, changes can alsooccur to genes that affect gene expression; changes caused by mechanismsother than genetic mutations. Epigenetics is the study of changes ingene expression caused by mechanisms other than changes in theunderlying DNA sequence, the methylation of DNA being one of thosemechanisms. Methylation of DNA, for example, the addition of a methylgroup to the 5 position of a cytosine pyrimidine ring or the positionalsixth nitrogen of an adenine purine ring, is widespread and plays acritical role in the regulation of gene expression in development anddifferentiation of diseases such as multiple sclerosis, diabetes,schizophrenia, aging, and cancers. In adult somatic cells, DNAmethylation typically occurs in regions where a cytosine nucleotide (C)is found next to a guanine nucleotide (G) where the C and G are linkedby a phosphate group (p), the linear construct being referred to as a“CpG” site. Methylation in particular gene regions, for example, in genepromoter regions, can augment or inhibit the expression of these genes.

DNA methylation is widespread and plays a critical role in theregulation of gene expression in development, differentiation anddisease. Methylation in particular regions of genes, for example theirpromoter regions, can inhibit the expression of these genes (Baylin andHerman (2000) DNA hypermethylation in tumorigenesis: epigenetics joinsgenetics. Trends Genet, 16, 168-174.; Jones and Laird (1999) Cancerepigenetics comes of age. Nat Genet, 21, 163-167). Gene silencingeffects of methylated regions has been shown to be accomplished throughthe interaction of methylcytosine binding proteins with other structuralcompounds of the chromatin (Razin (1998) CpG methylation, chromatinstructure and gene silencing—a three-way connection. Embo J, 17,4905-4908.; Yan et al. (2001) Role of DNA methylation and histoneacetylation in steroid receptor expression in breast cancer. J MammaryGland Biol Neoplasia, 6, 183-192), which, in turn, makes the DNAinaccessible to transcription factors through histone deacetylation andchromatin structure changes (Bestor (1998) Gene silencing. Methylationmeets acetylation. Nature, 393, 311-312). Genomic imprinting in whichimprinted genes are preferentially expressed from either the maternal orpaternal allele also involves DNA methylation. Deregulation ofimprinting has been implicated in several developmental disorders (Kumar(2000) Rett and ICF syndromes: methylation moves into medicine. JBiosci, 25, 213-214.; Sasaki et al. (1993) DNA methylation and genomicimprinting in mammals. Exs, 64, 469-486.; Zhong et al. (1996) A surveyof FRAXE allele sizes in three populations. Am J Med Genet, 64,415-419). The references cited above are incorporated herein byreference.

In vertebrates, the DNA methylation pattern is established early inembryonic development and in general the distribution of5-methylcytosine (5 mC) along the chromosome is maintained during thelife span of the organism (Razin and Cedar (1993) DNA methylation andembryogenesis. Exs, 64, 343-357.; Reik et al. (2001) Epigeneticreprogramming in mammalian development. Science, 293, 1089-1093, each ofwhich is incorporated herein by reference). Stable transcriptionalsilencing is important for normal development, and is associated withseveral epigenetic modifications. If methylation patterns are notproperly established or maintained, various disorders like mentalretardation, immune deficiency and sporadic or inherited cancers mayfollow.

Changes in DNA methylation have been recognized as one of the mostcommon molecular alterations in human neoplasia. Hypermethylation of CpGsites located in promoter regions of tumor suppressor genes is afrequent mechanism for gene inactivation in cancers. Hypomethylation ofgenomic DNA are observed in tumor cells. Further, a correlation betweenhypomethylation and increased gene expression has been reported for manyoncogenes. Monitoring global changes in methylation pattern has beenapplied to molecular classification of cancers, for example, genehypermethylation has been associated with clinical risk groups inneuroblastoma and hormone receptor status correlation with response totamoxifen in breast cancer.

In addition to playing an important role in cancer detection, a properunderstanding of genetic methylation patterns has been used to detectother conditions. The initiation and the maintenance of the inactiveX-chromosome in female eutherians were found to depend on methylation(Goto and Monk (1998) Regulation of X-chromosome inactivation indevelopment in mice and humans. Microbiol Mol Biol Rev, 62, 362-378,which is incorporated herein by reference). Rett syndrome (RTT) is anX-linked dominant disease caused by mutation of MeCP2 gene, which isfurther complicated by X-chromosome inactivation (XCI) pattern. Acurrent model predicts that MeCP2 represses transcription by bindingmethylated CpG residues and mediating chromatin remodeling (Dragich etal. (2000) Rett syndrome: a surprising result of mutation in MECP2. HumMol Genet, 9, 2365-2375, which is incorporated herein by reference).

Several technical challenges hinder development of methylation detectiontechniques into a robust and cost efficient screening tool. For example,the accuracy and affordability of currently available techniques can becompromised by impurities in samples that are to be tested. As a result,cumbersome and expensive purification techniques are often employed topurify a genomic sample from background nucleic acids. For example,tumor biopsy techniques are employed to physically separate tumortissues from healthy tissues. Depending upon the depth of the tissue inthe body of an individual, biopsy can require unpleasant and riskyharvesting procedures such as needle biopsy, endoscopy, bronchoscopy,colonoscopy or surgery. The presence of circulating tumor DNA in bloodprovides an attractive alternative to such biopsy techniques. However,circulating tumor DNA is typically present in low quantities and in abackground of a relatively large quantity of non-tumor DNA.

Thus there is a need for methods to distinguish methylation patterns incomplex genomic samples from particular tissues of interest (e.g. tumorDNA), often in a background of other genomic material from other tissues(e.g. circulating DNA). The methods and apparatus set forth hereinsatisfy this need and provide other advantages as well.

BRIEF SUMMARY

The present disclosure provides a method for distinguishing an aberrantmethylation level for DNA from a first cell type. The method can includesteps of (a) providing a test data set that includes (i) methylationstates for a plurality of sites from test genomic DNA from at least onetest organism, and (ii) coverage at each of the sites for detection ofthe methylation states; (b) providing methylation states for theplurality of sites in reference genomic DNA from one or more referenceindividual organisms, (c) determining, for each of the sites, themethylation difference between the test genomic DNA and the referencegenomic DNA, thereby providing a normalized methylation difference foreach site; and (d) weighting the normalized methylation difference foreach site by the coverage at each of the sites, thereby determining anaggregate coverage-weighted normalized methylation difference score.

Also provided is a method for distinguishing an aberrant methylationlevel for DNA from a sample containing DNA from a plurality of differentcell types, including steps of (a) providing a sample containing amixture of genomic DNA from a plurality of different cell types from atleast one test organism, thereby providing test genomic DNA; (b)detecting methylation states for a plurality of sites in the testgenomic DNA; (c) determining the coverage at each of the sites for thedetecting of the methylation states; (d) providing methylation statesfor the plurality of sites in reference genomic DNA from at least onereference individual, the at least one test organism and referenceindividual optionally being the same species; (e) determining, for eachof the sites, the methylation difference between the test genomic DNAand the reference genomic DNA, thereby providing a normalizedmethylation difference for each site; and (f) weighting the normalizedmethylation difference for each site by the coverage at each of thesites, thereby determining an aggregate coverage-weighted normalizedmethylation difference score.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include steps of(a) providing a mixture of genomic DNA from blood of an individualsuspected of having the condition (e.g. cancer), wherein the mixturecomprises genomic DNA from a plurality of different cell types from theindividual, thereby providing test genomic DNA; (b) detectingmethylation states for a plurality of sites in the test genomic DNA; (c)determining the coverage at each of the sites for the detecting of themethylation states; (d) providing methylation states for the pluralityof sites in reference genomic DNA from at least one referenceindividual, the reference individual being known to have the condition(e.g. cancer) or known to not have the condition (e.g. cancer); (e)determining, for each of the sites, the methylation difference betweenthe test genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site; (f) weighting thenormalized methylation difference for each site by the coverage at eachof the sites, thereby determining an aggregate coverage-weightednormalized methylation difference score; and (g) determining that theindividual does or does not have the condition (e.g. cancer) based onthe aggregate coverage-weighted normalized methylation difference score.

The present disclosure also provides an alternative sensitive method fordistinguishing an aberrant methylation level for DNA from a first celltype. The method can include a first stage of establishing a methylationbaseline, including the steps of (a) providing methylation states for aplurality of sites in baseline genomic DNA from two or more normalindividual organisms; and (b) determining, for each of the sites, themean methylation level and standard deviation of methylation levels forthe baseline genomic DNA; a second stage of determining aggregatemethylation scores for a plurality of training samples, including thesteps of (c) providing a training set of normal genomic DNA samples fromtwo or more normal individual organisms that includes (i) methylationstates for a plurality of sites in the training set of normal genomicDNA samples, and optionally (ii) coverage at each of the sites fordetection of the methylation states; (d) determining, for each of thesites, the methylation difference between each normal genomic DNA sampleof the training set and the baseline genomic DNA, thereby providing anormalized methylation difference for each normal genomic DNA sample ofthe training set at each site; (e) converting the normalized methylationdifference for each normal genomic DNA sample of the training set ateach site into the probability of observing such a normalizedmethylation difference or greater, and optionally weighting theprobability of such an event; (f) determining an aggregate methylationscore for each normal genomic DNA sample of the training set to obtaintraining set methylation scores; and (g) calculating the meanmethylation score and standard deviation of the training set methylationscores; a third stage, which can be carried out before, after, orconcurrently with the second stage, of determining an aggregatemethylation score for a given test sample, including the steps of (h)providing a test data set that includes (i) methylation states for theplurality of sites from test genomic DNA from at least one testorganism, and optionally (ii) coverage at each of the sites fordetection of the methylation states; (i) determining, for each of thesites, the methylation difference between the test genomic DNA and thebaseline genomic DNA, thereby providing a normalized methylationdifference for the test genomic DNA; (j) converting the normalizedmethylation difference for the test genomic DNA at each of the sitesinto the probability of observing such a normalized methylationdifference or greater, and optionally weighting the probability of suchan event; and (k) determining an aggregate methylation score for thetest genomic DNA; and a fourth stage of (1) comparing the methylationscore of the test genomic DNA to the mean methylation score and standarddeviation of methylation scores in the training set of normal genomicDNA to determine the number of standard deviations the methylation scoreof the test genomic DNA is from the distribution of methylation scoresin the training set of normal genomic DNA.

Also provided is an alternative sensitive method for distinguishing anaberrant methylation level for DNA from a sample containing DNA from aplurality of different cell types. The method can include a first stageof establishing a methylation baseline, including the steps of (a)providing methylation states for a plurality of sites in baselinegenomic DNA from two or more normal individual organisms; and (b)determining, for each of the sites, the mean methylation level andstandard deviation of methylation levels for the baseline genomic DNA; asecond stage of determining aggregate methylation scores for a pluralityof training samples, including the steps of (c) providing a training setof normal genomic DNA samples from two or more normal individualorganisms that includes (i) methylation states for a plurality of sitesin the training set of normal genomic DNA samples, and optionally (ii)coverage at each of the sites for detection of the methylation states;(d) determining, for each of the sites, the methylation differencebetween each normal genomic DNA sample of the training set and thebaseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site; (e) converting the normalized methylation difference for eachnormal genomic DNA sample of the training set at each site into theprobability of observing such a normalized methylation difference orgreater, and optionally weighting the probability; (f) determining anaggregate methylation score for each normal genomic DNA sample of thetraining set to obtain training set methylation scores; and (g)calculating the mean methylation score and standard deviation of thetraining set methylation scores; a third stage, which can be carried outbefore, after, or concurrently with the second stage, of determining anaggregate methylation score for a given test sample, including the stepsof (h) providing a mixture of genomic DNA from a test organism suspectedof having a condition associated with an aberrant DNA methylation level,wherein the mixture includes genomic DNA from a plurality of differentcell types from the test organism, thereby providing test genomic DNA;(i) detecting methylation states for the plurality of sites in the testgenomic DNA, and optionally determining the coverage at each of thesites for the detecting of the methylation states; (j) determining, foreach of the sites, the methylation difference between the test genomicDNA and the baseline genomic DNA, thereby providing a normalizedmethylation difference for the test genomic DNA; (k) converting thenormalized methylation difference for the test genomic DNA at each ofthe sites into the probability of observing such a normalizedmethylation difference or greater, and optionally weighting theprobability of such an event; and (1) determining an aggregatemethylation score for the test genomic DNA; and a fourth stage of (m)comparing the methylation score of the test genomic DNA to the meanmethylation score and standard deviation of methylation scores in thetraining set of normal genomic DNA to determine the number of standarddeviations the methylation score of the test genomic DNA is from thedistribution of methylation scores in the training set of normal genomicDNA.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include a firststage of establishing a methylation baseline, including the steps of (a)providing methylation states for a plurality of sites in baselinegenomic DNA from at least one normal individual organism; and (b)determining, for each of the sites, the mean methylation level andstandard deviation of methylation levels for the baseline genomic DNA; asecond stage of determining aggregate methylation scores for a pluralityof training samples, including the steps of (c) providing a training setof normal genomic DNA samples from two or more normal individualorganisms that includes (i) methylation states for a plurality of sitesin the training set of normal genomic DNA samples, and optionally (ii)coverage at each of the sites for detection of the methylation states;(d) determining, for each of the sites, the methylation differencebetween each normal genomic DNA sample of the training set and thebaseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site; (e) converting the normalized methylation difference for eachnormal genomic DNA sample of the training set at each site into theprobability of observing such a normalized methylation difference orgreater, and optionally weighting the probability of such an event; (f)determining a methylation score for each normal genomic DNA sample ofthe training set to obtain training set methylation scores; and (g)calculating the mean methylation score and standard deviation of thetraining set methylation scores; a third stage, which can be carried outbefore, after, or concurrently with the second stage, of determining anaggregate methylation score for a given test sample, including the stepsof (h) providing a mixture of genomic DNA from a test organism suspectedof having the condition, wherein the mixture comprises genomic DNA froma plurality of different cell types from the test organism, therebyproviding test genomic DNA; (i) detecting methylation states for theplurality of sites in the test genomic DNA, and optionally determiningthe coverage at each of the sites for the detecting of the methylationstates; (j) determining, for each of the sites, the methylationdifference between the test genomic DNA and the baseline genomic DNA,thereby providing a normalized methylation difference for the testgenomic DNA; (k) converting the normalized methylation difference forthe test genomic DNA at each of the sites into the probability ofobserving such a normalized methylation difference or greater, andoptionally weighting the probability of such an event; and (l)determining a methylation score for the test genomic DNA; and a fourthstage of (m) comparing the methylation score of the test genomic DNA tothe mean methylation score and standard deviation of methylation scoresin the training set of normal genomic DNA to determine the number ofstandard deviations the methylation score of the test genomic DNA isfrom the distribution of methylation scores in the training set ofnormal genomic DNA.

The present disclosure provides a method for using methylation levels toidentify or classify a specific type of cancer in a test organism. Themethod can include a first stage of identifying specific cancers thatcan be used as a cancer type, including (a) providing a data set thatincludes methylation states for a plurality of sites from genomic DNAfrom clinical samples known to include a specific cancer; a second stageof selecting hypermethylated sites that includes (b) identifyinghypermethylated sites characteristic of a cancer type, including (i)determining a mean methylation level for each site in the genomic DNA ofthe clinical samples known to include the specific cancer, (ii)determining which sites meet a first threshold, a second threshold, or acombination thereof, where determining the first threshold includes (1)determining the absolute value of the mean methylation level of eachsite; (2) ranking the mean methylation levels for each site from lowestto highest, and (3) selecting those sites having a mean methylationlevel at a percentile rank that is greater than or equivalent to a firstpreselected value, and where determining the second threshold includes(1) determining the absolute value of the mean methylation level of eachsite; and (2) selecting those sites having a mean methylation level thatis greater than a second preselected value, and (iii) compiling a listof hypermethylated sites that are characteristic for the cancer type;and (c) repeating (a) and (b) for each specific cancer, to result in aplurality of lists of hypermethylated sites that are characteristic foradditional cancer types; a third stage that includes analyzing a testgenomic DNA sample from a test organism by (d) providing a test data setthat includes a methylation level for each hypermethylated site from atest genomic DNA from an individual test organism, wherein thehypermethylated sites are from one of the lists of hypermethylated sitesthat is characteristic for a cancer type identified in steps (b) and(c); (e) averaging the methylation level of each of the hypermethylatedsites to result in a single average methylation level for the testgenomic DNA for the cancer type identified in steps (b) and (c); (f)repeating step (e) for each cancer type, to result in an averagemethylation level for each cancer type; and (g) ranking the averagemethylation levels for each cancer type from lowest to highest, whereinthe cancer type corresponding to the highest average methylation levelis the cancer present in the individual test organism.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows criteria for designing probes to regions of a genome havingmethylation sites. ACGATCGCCTTCGACCGTCGAA (SEQ ID NO:1);AUGATUGUUTTCGAUCGTUGAA (SEQ ID NO:2); ATGATTGTTTTCGATCGTTGAA (SEQ IDNO:3); ATGATAGCCTTCGACCGTAGAA (SEQ ID NO:4); ATGATAGUUTTUGACCGTAGAA (SEQID NO:5); ATGATAGTTTTTGACCGTAGAA (SEQ ID NO:6); TTCGACGATCGAAAACGATCGT(SEQ ID NO:7); TTCAACAATCAAAAACAATCAT (SEQ ID NO:8); andTTCTACRGTCRAAAACTATCAT (SEQ ID NO:9).

FIG. 2 shows a workflow for targeted circulating tumor DNA (ctDNA)methylation sequencing.

FIG. 3 shows aggregate coverage-weighted normalized methylationdifferences (z-scores) determined as described herein for various cancersamples at various titration levels.

FIG. 4 shows coverage-weighted methylation scores determined asdescribed herein for colorectal cancer samples at various titrationlevels.

FIG. 5 shows methylation scores for 66 samples from advanced cancerpatients and 25 samples from normal individuals, demonstrating theability of the methylation score algorithm to distinguish advancedcancer samples from normal samples.

FIG. 6 shows a tabulated summary of the methylation scores shown in FIG.5.

FIG. 7 shows correlation of methylation profiles between plasma andtissue DNA samples from cancer patients.

FIG. 8 shows cancer type classification results on tumor tissue samples,demonstrating the ability of the cancer type classification algorithm toidentify most tumors based on DNA methylation data with a high degree ofaccuracy.

FIG. 9 depicts cancer type classification results on plasma DNA samplesfrom cancer patients, showing high clinical sensitivities for colorectaland breast cancers.

DETAILED DESCRIPTION

DNA methylation data can provide valuable information, when evaluatedindependently or in combination with other information such as genotypeor gene expression patterns. One object of the methods set forth hereinis to determine this information, e.g. if one or more sites in a genomeare differentially methylated in a test sample compared to a referencesample or data set.

Particular embodiments can be used for the detection, screening,monitoring (e.g. for relapse, remission, or response to treatment),staging, classification (e.g. for aid in choosing the most appropriatetreatment modality) and prognostication of cancer using methylationanalysis of circulating plasma/serum DNA.

Cancer DNA is known to demonstrate aberrant DNA methylation (see, forexample, Herman et al. 2003 N Engl J Med 349: 2042-2054, which isincorporated herein by reference). For example, the CpG site promotersof genes, e.g. tumor suppressor genes, are hypermethylated while the CpGsites in the gene body are hypomethylated when compared with non-cancercells. In particular embodiments of the methods set forth herein, amethylation pattern detected from the blood of an individual suspectedof having cancer is indicative of the methylation state of potentiallycancerous tissues such that the pattern is expected to be differentbetween individuals with cancer when compared with those healthyindividuals without cancer or when compared with those whose cancer hasbeen cured.

Because aberrant methylation occurs in most cancers, the methodsdescribed herein can be applied to the detection of any of a variety ofmalignancies with aberrant methylation, for example, malignancies inlung, breast, colorectum, prostate, nasopharynx, stomach, testes, skin,nervous system, bone, ovary, liver, hematologic tissues, pancreas,uterus, kidney, lymphoid tissues, etc. The malignancies may be of avariety of histological subtypes, for example, carcinomas,adenocarcinomas, sarcomas, fibroadenocarcinoma, neuroendocrine, orundifferentiated.

In particular embodiments, a method for determining methylation patternscan be used to monitor development of a fetus (e.g. to determine thepresence or absence of a developmental abnormality) or to determine thepresence of a particular disease or condition. In such cases the methodcan be carried out using a sample (e.g. blood, tissue or amniotic fluid)obtained from a pregnant female and the sample can be evaluated formethylation levels of fetal nucleic acids. A DNA methylation profile ofplacental tissues can be used to evaluate the pathophysiology ofpregnancy-associated or developmentally-related diseases, such aspreeclampsia and intrauterine growth restriction. Disorders in genomicimprinting are associated with developmental disorders, such asPrader-Willi syndrome and Angelman syndrome, and can be identified orevaluated using methods of the present disclosure. Altered profiles ofgenomic imprinting and global DNA methylation in placental and fetaltissues have been observed in pregnancies resulting from assistedreproductive techniques (see, for example, Hiura et al. 2012 Hum Reprod;27: 2541-2548, incorporated herein by reference) and can be detectedusing methods set forth herein. Exemplary methods that can be modifiedfor use with the methods of the present disclosure are forth in US Pat.App. Pub. Nos. 2013/0189684 A1 or 2014/0080715 A1, each of which isincorporated herein by reference.

The ability to determine placental or fetal methylation patterns frommaternal plasma provides a noninvasive method to determine, detect andmonitor pregnancy-associated conditions such as preeclampsia,intrauterine growth restriction, preterm labor and others. For example,the detection of a disease-specific aberrant methylation signatureallows the screening, diagnosis and monitoring of suchpregnancy-associated conditions.

Additionally, a method set forth herein to obtain diagnostic orprognostic information for other conditions. For example, liver tissuecan be analyzed to determine a methylation pattern specific to theliver, which may be used to identify liver pathologies. Other tissueswhich can also be analyzed include brain cells, bones, the lungs, theheart, the muscles and the kidneys, etc. DNA can be obtained from bloodsamples and analyzed in a method set forth herein in order to determinethe state of any of a variety of tissues that contribute DNA to theblood.

Furthermore, methylation patterns of transplanted organs can bedetermined from plasma DNA of organ transplantation recipients.Transplant analysis from plasma, can be a synergistic technology totransplant genomic analysis from plasma, such as technology set forth inZheng at al. 2012 Clin Chem 58: 549-558; Lo at al. 1998 Lancet 351:1329-1330; or Snyder et al. 2011 Proc Natl Acad Sci USA; 108: 6229-6234,each of which is incorporated herein by reference.

The methylation patterns of various tissues may change from time totime, e.g. as a result of development, aging, disease progression (e.g.inflammation, cancer or cirrhosis) or treatment. The dynamic nature ofDNA methylation makes such analysis potentially very valuable formonitoring of physiological and pathological processes. For example, ifone detects a change in the plasma methylation pattern of an individualcompared to a baseline value obtained when they were healthy, one couldthen detect disease processes in organs that contribute plasma DNA.

Terms used herein will be understood to take on their ordinary meaningin the relevant art unless specified otherwise. Several terms usedherein and their meanings are set forth below.

As used herein, the term “cell-free,” when used in reference to DNA, isintended to mean DNA that has been removed from a cell in vivo. Theremoval of the DNA can be a natural process such as necrosis orapoptosis. Cell-free DNA is generally obtained from blood, or a fractionthereof, such as plasma. Cell-free DNA can be obtained from other bodilyfluids or tissues.

As used herein, the term “cell type” is intended to identify cells basedon morphology, phenotype, developmental origin or other known orrecognizable distinguishing cellular characteristic. A variety ofdifferent cell types can be obtained from a single organism (or from thesame species of organism). Exemplary cell types include, but are notlimited to urinary bladder, pancreatic epithelial, pancreatic alpha,pancreatic beta, pancreatic endothelial, bone marrow lymphoblast, bonemarrow B lymphoblast, bone marrow macrophage, bone marrow erythroblast,bone marrow dendritic, bone marrow adipocyte, bone marrow osteocyte,bone marrow chondrocyte, promyeloblast, bone marrow megakaryoblast,bladder, brain B lymphocyte, brain glial, neuron, brain astrocyte,neuroectoderm, brain macrophage, brain microglia, brain epithelial,cortical neuron, brain fibroblast, breast epithelial, colon epithelial,colon B lymphocyte, mammary epithelial, mammary myoepithelial, mammaryfibroblast, colon enterocyte, cervix epithelial, ovary epithelial, ovaryfibroblast, breast duct epithelial, tongue epithelial, tonsil dendritic,tonsil B lymphocyte, peripheral blood lymphoblast, peripheral blood Tlymphoblast, peripheral blood cutaneous T lymphocyte, peripheral bloodnatural killer, peripheral blood B lymphoblast, peripheral bloodmonocyte, peripheral blood myeloblast, peripheral blood monoblast,peripheral blood promyeloblast, peripheral blood macrophage, peripheralblood basophil, liver endothelial, liver mast, liver epithelial, liver Blymphocyte, spleen endothelial, spleen epithelial, spleen B lymphocyte,liver hepatocyte, liver Alexander, liver fibroblast, lung epithelial,bronchus epithelial, lung fibroblast, lung B lymphocyte, lung Schwann,lung squamous, lung macrophage, lung osteoblast, neuroendocrine, lungalveolar, stomach epithelial and stomach fibroblast. In someembodiments, two cells can be considered to be the same type of celldespite one of the cells having been phenotypically or morphologicallyaltered by a condition or disease such as cancer. For purposes ofcomparison, a first cell that has been altered by a disease or conditioncan be compared to a second cell based on the known or suspected stateof the first cell prior to having been altered. For example, a cancerouspancreatic ductal epithelium cell can be considered to be the same typeof cell as a non-cancerous pancreatic ductal epithelium cell.

As used herein, the term “circulating,” when used in reference to DNA,is intended to mean DNA that is or was moving through the circulatorysystem of an organism, whether in cell-free form or inside circulatingcells.

As used herein, the term “coverage,” when used in reference to a geneticlocus, is intended to mean the number of detection events (e.g. sequencereads) that align to, or “cover,” the locus. In some embodiments, theterm refers to the average number of detection events (e.g. sequencereads) that align to, or “cover,” a plurality of loci. Generally, thecoverage level obtained from a sequencing method correlates directlywith the degree of confidence in the accuracy of the call (e.g.nucleotide type or methylation state) determined at a particular baseposition or genetic locus. At higher levels of coverage, a locus iscovered by a greater number of aligned sequence reads, so calls can bemade with a higher degree of confidence.

As used herein, the term “CpG site” is intended to mean the location ina nucleic acid molecule, or sequence representation of the molecule,where a cytosine nucleotide and guanine nucleotide occur, the 3′ oxygenof the cytosine nucleotide being covalently attached to the 5′ phosphateof the guanine nucleotide. The nucleic acid is typically DNA. Thecytosine nucleotide can optionally contain a methyl moiety,hydroxymethyl moiety or hydrogen moiety at position 5 of the pyrimidinering.

As used herein, the term “derived,” when used in reference to DNA, isintended to refer to the source from which the DNA was obtained or theorigin where the DNA was synthesized. In the case of biologicallyderived DNA, the term can be used to refer to an in vivo source fromwhich the DNA was obtained or the in vivo origin where the DNA wassynthesized. Exemplary origins include, but are not limited to, a cell,cell type, tissue, tissue type, organism or species of organism. In thecase of synthetically derived DNA, the term can be used to refer to anin vitro source from which the DNA was obtained or the in vitro originwhere the DNA was synthesized. A DNA molecule that is derived from aparticular source or origin can nonetheless be subsequently copied oramplified. The sequence of the resulting copies or amplicons can bereferred to as having been derived from the source or origin.

As used herein, the term “each,” when used in reference to a collectionof items, is intended to identify an individual item in the collectionbut does not necessarily refer to every item in the collection.Exceptions can occur if explicit disclosure or context clearly dictatesotherwise.

As used herein, the term “methylation difference” is intended to mean aqualitative or quantitative indicia that two nucleotides or nucleicacids do not have the same methylation state. The methylation differencecan be indicated for nucleotides that are at aligned positions ondifferent nucleic acids. In some cases the methylation difference can bea sum or aggregate of a plurality of aligned positions. When two or morenucleic acids are aligned, the methylation difference can be an averageacross one or more aligned positions.

As used herein, the term “methylation state,” when used in reference toa locus (e.g., a CpG site or polynucleotide segment) across severalmolecules having that locus, refers to one or more characteristics ofthe locus relevant to presence or absence of a methyl moiety.Non-limiting examples of such characteristics include whether any of thecytosine (C) bases within a locus are methylated, location of methylatedC base(s), percentage of methylated C base(s) at a particular locus, andallelic differences in methylation due to, for example, difference inthe origin of alleles. Reference to the methylation state of aparticular CpG site in a nucleic acid molecule, is directed to thepresence or absence of a methyl moiety at position 5 of the pyrimidinering of a cytosine. The term can be applied to one or more cytosinenucleotides (or representations thereof e.g. a chemical formula), or toone or more nucleic acid molecules (or representations thereof e.g. asequence representation). The term can also refer to the relative orabsolute amount (e.g., concentration) of methylated C or non-methylatedC at a particular locus in a nucleic acid. A methylation state sometimesis hypermethylated and sometimes is hypomethylated. For example, if allor a majority of C bases within a locus are methylated, the methylationstate can be referred to as “hypermethylated.” In another example, ifall or a majority of C bases within a locus are not methylated, themethylation state may be referred to as “hypomethylated.” Likewise, ifall or a majority of C bases within a locus are methylated as comparedto reference then the methylation state is considered hypermethylatedcompared to the reference. Alternatively, if all or a majority of the Cbases within a locus are not methylated as compared to a reference thenthe methylation state is considered hypomethylated compared to thereference.

A “methylation site” is a locus in a nucleic acid where methylation hasoccurred, or has the possibility of occurring. A methylation sitesometimes is a C base, or multiple C bases in a region, and sometimes amethylation site is a CpG site in a locus. Each methylation site in thelocus may or may not be methylated. A methylation site can besusceptible to methylation by a naturally occurring event in vivo or byan event that chemically methylates a nucleotide in vitro.

As used herein, the term “mixture,” when used in reference to two ormore components, is intended to mean that the two or more components aresimultaneously present in a fluid or vessel. The components aretypically capable of contacting each other via diffusion or agitation.The components may be separate molecules (e.g. two or more nucleic acidfragments) or the components may be part of a single molecule (e.g.sequence regions on a long nucleic acid molecule).

As used herein, the term “tissue” is intended to mean a collection oraggregation of cells that act together to perform one or more specificfunctions in an organism. The cells can optionally be morphologicallysimilar. Exemplary tissues include, but are not limited to, eye, muscle,skin, tendon, vein, artery, blood, heart, spleen, lymph node, bone, bonemarrow, lung, bronchi, trachea, gut, small intestine, large intestine,colon, rectum, salivary gland, tongue, gall bladder, appendix, liver,pancreas, brain, stomach, skin, kidney, ureter, bladder, urethra, gonad,testicle, ovary, uterus, fallopian tube, thymus, pituitary, thyroid,adrenal, or parathyroid. Tissue can be derived from any of a variety oforgans of a human or other body.

The embodiments set forth below and recited in the claims can beunderstood in view of the above definitions.

The present disclosure provides a method for distinguishing an aberrantmethylation level for DNA from a first cell type. The method can includesteps of (a) providing a test data set that includes (i) methylationstates for a plurality of sites (e.g. CpG sites) from test genomic DNAfrom at least one test organism, and (ii) coverage at each of the sites(e.g. CpG sites) for detection of the methylation states; (b) providingmethylation states for the plurality of sites (e.g. CpG sites) inreference genomic DNA from one or more reference individual organisms,(c) determining, for each of the sites (e.g. CpG sites), the methylationdifference between the test genomic DNA and the reference genomic DNA,thereby providing a normalized methylation difference for each sites(e.g. CpG sites); and (d) weighting the normalized methylationdifference for each sites (e.g. CpG sites) by the coverage at each ofthe sites (e.g. CpG sites), thereby determining an aggregatecoverage-weighted normalized methylation difference score. Optionallythe sites from the test genomic DNA are derived from a plurality ofdifferent cell types from the individual test organism and as a furtheroption the cell type from which each of the sites is derived is unknown.In a further optional embodiment, the individual test organism and theone or more reference individual organisms are the same species.

Also provided is a method for distinguishing an aberrant methylationlevel for DNA from a sample containing DNA from a plurality of differentcell types, including steps of (a) providing a sample containing amixture of genomic DNA from a plurality of different cell types from atleast one test organism, thereby providing test genomic DNA; (b)detecting methylation states for a plurality of sites (e.g. CpG sites)in the test genomic DNA; (c) determining the coverage at each of thesites (e.g. CpG sites) for the detecting of the methylation states; (d)providing methylation states for the plurality of sites (e.g. CpG sites)in reference genomic DNA from at least one reference individual, the atleast one test organism and reference individual optionally being thesame species; (e) determining, for each of the sites (e.g. CpG sites),the methylation difference between the test genomic DNA and thereference genomic DNA, thereby providing a normalized methylationdifference for each site (e.g. CpG site); and (f) weighting thenormalized methylation difference for each site (e.g. CpG site) by thecoverage at each of the sites (e.g. CpG sites), thereby determining anaggregate coverage-weighted normalized methylation difference score.

The present invention also provides an alternative sensitive method fordistinguishing an aberrant methylation level for DNA from a first celltype.

The first stage of this method involves establishing a methylationbaseline, including the steps of (a) providing methylation states for aplurality of sites (e.g., CpG sites) in baseline genomic DNA from two ormore normal individual organisms; and (b) determining, for each of thesites (e.g., CpG sites), the mean methylation level and standarddeviation of methylation levels for the baseline genomic DNA. In someembodiments, the number of normal individual organisms providingbaseline genomic DNA is at least 3, at least 5, at least 10, at least20, at least 50, or at least 100.

The second stage of this method involves determining aggregatemethylation scores for a plurality of training samples, including thesteps of (c) providing a training set of normal genomic DNA samples fromtwo or more normal individual organisms that includes (i) methylationstates for a plurality of sites (e.g., CpG sites) in the training set ofnormal genomic DNA samples, and optionally (ii) coverage at each of thesites (e.g., CpG sites) for detection of the methylation states; (d)determining, for each of the sites (e.g., CpG sites), the methylationdifference between each normal genomic DNA sample of the training setand the baseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG site); (e) converting the normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG site) into the probability of observing such anormalized methylation difference or greater (e.g., a one-sidedp-value), and optionally weighting the probability of such an event; (f)determining an aggregate methylation score for each normal genomic DNAsample of the training set to obtain training set methylation scores;and (g) calculating the mean methylation score and standard deviation ofthe training set methylation scores. In some embodiments, the number ofnormal individual organisms providing genomic DNA for the training setis at least 3, at least 5, at least 10, at least 20, at least 50, or atleast 100.

The third stage of this method, which can be carried out before, after,or concurrently with the second stage, involves determining an aggregatemethylation score for a given test sample, including the steps of (h)providing a test data set that includes (i) methylation states for theplurality of sites (e.g., CpG sites) from test genomic DNA from at leastone test organism, and optionally (ii) coverage at each of the sites(e.g., CpG sites) for detection of the methylation states; (i)determining, for each of the sites (e.g., CpG sites), the methylationdifference between the test genomic DNA and the baseline genomic DNA,thereby providing a normalized methylation difference for the testgenomic DNA; (j) converting the normalized methylation difference forthe test genomic DNA at each of the sites (e.g., CpG sites) into theprobability of observing such a normalized methylation difference orgreater (e.g., a one-sided p-value), and optionally weighting theprobability of such an event; and (k) determining an aggregatemethylation score for the test genomic DNA.

The fourth and final stage of this method involves the step of (1)comparing the methylation score of the test genomic DNA to the meanmethylation score and standard deviation of methylation scores in thetraining set of normal genomic DNA to determine the number of standarddeviations the methylation score of the test genomic DNA is from thedistribution of methylation scores in the training set of normal genomicDNA. In the event the number of standard deviations exceeds apredetermined threshold value (e.g., 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0,etc.), the test sample is considered to have an aberrant DNA methylationlevel.

Optionally, the methylation sites (e.g., CpG sites) from the testgenomic DNA are derived from a plurality of different cell types fromthe individual test organism, and as a further option, the cell typefrom which each of the sites (e.g., CpG sites) is derived is unknown. Ina further optional embodiment, the individual test organism and the oneor more baseline individual organisms, training individual organisms, ora combination thereof are the same species.

Also provided is an alternative sensitive method for distinguishing anaberrant methylation level for DNA from a sample containing DNA from aplurality of different cell types.

The first stage of this method involves establishing a methylationbaseline, including the steps of (a) providing methylation states for aplurality of sites (e.g., CpG sites) in baseline genomic DNA from two ormore normal individual organisms; and (b) determining, for each of thesites (e.g., CpG sites), the mean methylation level and standarddeviation of methylation levels for the baseline genomic DNA. In someembodiments, the number of normal individual organisms providingbaseline genomic DNA is at least 3, at least 5, at least 10, at least20, at least 50, or at least 100.

The second stage of this method involves determining aggregatemethylation scores for a plurality of training samples, including thesteps of (c) providing a training set of normal genomic DNA samples fromtwo or more normal individual organisms that includes (i) methylationstates for a plurality of sites (e.g., CpG sites) in the training set ofnormal genomic DNA samples, and optionally (ii) coverage at each of thesites (e.g., CpG sites) for detection of the methylation states; (d)determining, for each of the sites (e.g., CpG sites), the methylationdifference between each normal genomic DNA sample of the training setand the baseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG site); (e) converting the normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG sites) into the probability of observing such anormalized methylation difference or greater (e.g., a one-sidedp-value), and optionally weighting the probability; (f) determining anaggregate methylation score for each normal genomic DNA sample of thetraining set to obtain training set methylation scores; and (g)calculating the mean methylation score and standard deviation of thetraining set methylation scores. In some embodiments, the number ofnormal individual organisms providing genomic DNA for the training setis at least 3, at least 5, at least 10, at least 20, at least 50, or atleast 100.

The third stage of this method, which can be carried out before, after,or concurrently with the second stage, involves determining an aggregatemethylation score for a given test sample, including the steps of (h)providing a mixture of genomic DNA from a test organism suspected ofhaving a condition associated with an aberrant DNA methylation level(e.g., cancer), wherein the mixture comprises genomic DNA from aplurality of different cell types from the test organism, therebyproviding test genomic DNA; (i) detecting methylation states for theplurality of sites (e.g., CpG sites) in the test genomic DNA, andoptionally determining the coverage at each of the sites (e.g., CpGsites) for the detecting of the methylation states; (j) determining, foreach of the sites (e.g., CpG sites), the methylation difference betweenthe test genomic DNA and the baseline genomic DNA, thereby providing anormalized methylation difference for the test genomic DNA; (k)converting the normalized methylation difference for the test genomicDNA at each of the sites (e.g., CpG sites) into the probability ofobserving such a normalized methylation difference or greater (e.g., aone-sided p-value), and optionally weighting the probability of such anevent; and (1) determining an aggregate methylation score for the testgenomic DNA.

The fourth and final stage of this method involves the step of (m)comparing the methylation score of the test genomic DNA to the meanmethylation score and standard deviation of methylation scores in thetraining set of normal genomic DNA to determine the number of standarddeviations the methylation score of the test genomic DNA is from thedistribution of methylation scores in the training set of normal genomicDNA. In the event the number of standard deviations exceeds apredetermined threshold value (e.g., 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,5.0, etc.), the test sample is considered to have an aberrant DNAmethylation level.

A method set forth herein can be carried out for any of a variety oftest organisms. Exemplary organisms include, without limitation,eukaryotic (unicellular or multicellular) organisms. Exemplaryeukaryotic organisms include a mammal such as a rodent, mouse, rat,rabbit, guinea pig, ungulate, horse, sheep, pig, goat, cow, cat, dog,primate, human or non-human primate; a plant such as Arabidopsisthaliana, corn (Zea mays), sorghum, oat (Oryza sativa), wheat, rice,canola, or soybean; an algae such as Chlamvdomonas reinhardtii; anematode such as Caenorhabditis elegans; an insect such as Drosophilamelanogaster, mosquito, fruit fly, honey bee or spider; a fish such aszebrafish (Danio rerio); a reptile: an amphibian such as a frog orXenopus laevis; a Dictyostelium discoideum; a fungi such as Pneumocystiscarinii, Takifugu rubripes, yeast such as Saccharamoyces cerevisiae orSchizosaccharomyces pombe; or a Plasmodium falciparum. A method of thepresent disclosure can also be used to evaluate methylation in organismssuch as prokaryotes, examples of which include a bacterium, Escherichiacoli, Staphylococci or Mycoplasma pneumoniae; an archae; a virus,examples of which include Hepatitis C virus or human immunodeficiencyvirus; or a viroid.

Particular embodiments of the methods set forth herein can provideadvantages when applied to multicellular organisms because the methodsprovide for determination of the methylation states for genomic DNAderived from a particular cell or tissue in a background of nucleicacids derived from other cells or tissues. Thus, the methods set forthherein can be particularly useful for mammals, such as humans. In somecases the methods can be carried out on samples containing nucleic acidmixtures from several different cell types or tissue types such assamples obtained from the blood or other biological fluid of amulticellular organism. Furthermore, the methods set forth herein can beadvantageously employed for evaluation of methylation states for genomicDNA obtained from somatic cells of a pregnant female mammal, such as apregnant female human, and/or the methylation states for genomic DNAobtained from somatic cells of one or more prenatal offspring carried bythe female.

In some embodiments, the methods can be carried out for a mixture ofgenomic DNA from several different cell types from a mixed organismenvironment (e.g. metagenomics sample) such as an ecological sample(e.g. pond, ocean, thermal vent, etc.) or digestive system sample (e.g.mouth, gut, colon, etc.). Thus, the method can be carried out for amixed organism sample wherein individual species are not separated orcultivated.

As will be evident from several exemplary embodiments set forth herein,the CpG sites from a test genomic DNA that are evaluated in a method ofthis disclosure can optionally be derived from a plurality of differentcell types from the individual test organism. As a further option thecell type from which each of the CpG sites is derived need not be known.This will often be the case when the sample used in the method isderived from blood or another biological fluid or metagenomics sample.

In particular embodiments, the test sample used in a method set forthherein can include circulating tumor DNA and circulating non-tumor DNA.This can be the case when the test sample includes DNA obtained fromblood, for example, from an individual known or suspected to havecancer.

Particular embodiments of the methods set forth herein can be carriedout using methylation states for a plurality of sites from test genomicDNA from an individual test organism. In some cases the data is providedto an individual or system that carries out the method. Alternatively,embodiments of the methods can include one or more steps for detectingmethylation states for a plurality of sites in a test genome.

Methylation of sites, such as CpG dinucleotide sequences, can bemeasured using any of a variety of techniques used in the art for theanalysis of such sites. For example, methylation can be measured byemploying a restriction enzyme based technology, which utilizesmethylation sensitive restriction endonucleases for the differentiationbetween methylated and unmethylated cytosines. Restriction enzyme basedtechnologies include, for example, restriction digest withmethylation-sensitive restriction enzymes followed by nucleic acidsequencing (e.g. massively parallel or Next Generation sequencing),Southern blot analysis, real time PCR, restriction landmark genomicscanning (RLGS) or differential methylation hybridization (DMH).

Restriction enzymes characteristically hydrolyze DNA at and/or uponrecognition of specific sequences or recognition motifs that aretypically between 4- to 8-bases in length. Among such enzymes,methylation sensitive restriction enzymes are distinguished by the factthat they either cleave, or fail to cleave DNA according to the cytosinemethylation state present in the recognition motif, in particular, ofthe CpG sequences. In methods employing such methylation sensitiverestriction enzymes, the digested DNA fragments can be differentiallyseparated (e.g. based on size or hybridization affinity to complementaryprobes), differentially amplified (e.g. based on affinity to anamplification primer), or differentially detected (e.g. via a microarraydetection technique or nucleic acid sequencing technique) such that themethylation status of the sequence can thereby be deduced.

In some embodiments that employ methylation sensitive restrictionenzymes, a post-digest PCR amplification step is added wherein a set oftwo oligonucleotide primers, one on each side of the methylationsensitive restriction site, is used to amplify the digested genomic DNA.PCR products are produced and detected for templates that were notrestricted (e.g. due to presence of a methylated restriction site)whereas PCR products are not produced where digestion of the subtendedmethylation sensitive restriction enzyme site occurs. Techniques forrestriction enzyme based analysis of genomic methylation are well knownin the art and include the following: differential methylationhybridization (DMH) (Huang et al., 1999, Human Mol. Genet. 8, 459-70);Not I-based differential methylation hybridization (for example,WO02/086163A1); restriction landmark genomic scanning (RLGS) (Plass etal., 1999, Genomics 58:254-62); methylation sensitive arbitrarily primedPCR (AP-PCR) (Gonzalgo et al., 1997, Cancer Res. 57: 594-599);methylated CpG site amplification (MCA) (Toyota et. al., 1999, CancerRes. 59: 2307-2312). Other useful methods for detecting genomicmethylation are described, for example, in US Patent Applicationpublication 2003/0170684 A1 or WO 04/05122. The references cited aboveare incorporated herein by reference.

Methylation of CpG dinucleotide sequences can also be measured byemploying cytosine conversion based technologies, which rely onmethylation status-dependent chemical modification of CpG sequenceswithin isolated genomic DNA, or fragments thereof, followed by DNAsequence analysis. Chemical reagents that are able to distinguishbetween methylated and non-methylated CpG dinucleotide sequences includehydrazine, which cleaves the nucleic acid, and bisulfite. Bisulfitetreatment followed by alkaline hydrolysis specifically convertsnon-methylated cytosine to uracil, leaving 5-methylcytosine unmodifiedas described by Olek A., 1996, Nucleic Acids Res. 24:5064-6 or Frommeret al., 1992, Proc. Natl. Acad. Sci. USA 89:1827-1831, each of which isincorporated herein by reference. The bisulfite-treated DNA cansubsequently be analyzed by molecular techniques, such as PCRamplification, sequencing, and detection comprising oligonucleotidehybridization (e.g. using nucleic acid microarrays).

Techniques for the analysis of bisulfite treated DNA can employmethylation-sensitive primers for the analysis of CpG methylation statuswith isolated genomic DNA, for example, as described by Herman et al.,1996, Proc. Natl. Acad. Sci. USA 93:9821-9826, or U.S. Pat. No.5,786,146 or 6,265,171, each of which is incorporated herein byreference. Methylation sensitive PCR (MSP) allows for the detection of aspecific methylated CpG position within, for example, the regulatoryregion of a gene. The DNA of interest is treated such that methylatedand non-methylated cytosines are differentially modified, for example,by bisulfite treatment, in a manner discernable by their hybridizationbehavior. PCR primers specific to each of the methylated andnon-methylated states of the DNA are used in PCR amplification. Productsof the amplification reaction are then detected, allowing for thededuction of the methylation status of the CpG position within thegenomic DNA. Other methods for the analysis of bisulfite treated DNAinclude methylation-sensitive single nucleotide primer extension(Ms-SNuPE) (see, for example, Gonzalgo & Jones, 1997; Nucleic Acids Res.25:2529-2531, or U.S. Pat. No. 6,251,594, each of which is incorporatedherein by reference), or the use of real time PCR based methods, such asthe art-recognized fluorescence-based real-time PCR techniqueMethyLight™ (see, for example, Eads et al., 1999; Cancer Res.59:2302-2306, U.S. Pat. No. 6,331,393 or Heid et al., 1996, Genome Res.6:986-994, each of which is incorporated herein by reference). It willbe understood that a variety of methylation assay methods can be usedfor the determination of the methylation status of particular genomicCpG positions. Methods which employ bisulfite conversion include, forexample, bisulfite sequencing, methylation-specific PCR,methylation-sensitive single nucleotide primer extension (Ms-SnuPE),MALDI mass spectrometry and methylation-specific oligonucleotide arrays,for example, as described in U.S. Pat. No. 7,611,869 or InternationalPatent Application WO2004/051224, each of which is incorporated hereinby reference.

In particular embodiments, methylation of genomic CpG positions in asample can be detected using an array of probes. In such embodiments, aplurality of different probe molecules can be attached to a substrate orotherwise spatially distinguished in an array. Exemplary arrays that canbe used in the invention include, without limitation, slide arrays,silicon wafer arrays, liquid arrays, bead-based arrays and others knownin the art or set forth in further detail herein. In preferredembodiments, the methods of the invention can be practiced with arraytechnology that combines a miniaturized array platform, a high level ofassay multiplexing, and scalable automation for sample handling and dataprocessing. Particularly useful arrays are described in U.S. Pat. Nos.6,355,431; 6,429,027; 6,890,741; 6,913,884 or 7,582,420; or U.S. Pat.App. Pub. Nos. 2002/0102578 A1; 2005/0053980 A1; 2005/0181440 A1; or2009/0186349 A1, each of which is incorporated herein by reference.Further examples of useful arrays include those described in U.S. Pat.Nos. 6,023,540, 6,200,737 or 6,327,410; or PCT Pub. Nos. WO9840726,WO9918434 or WO9850782, each of which is incorporated herein byreference.

The plexity of an array used in the invention can vary depending on theprobe composition and desired use of the array. For example, the plexityof nucleic acids (or CpG sites) detected in an array can be at least 10,100, 1,000, 10,000, 0.1 million, 1 million, 10 million, 100 million ormore. Alternatively or additionally, the plexity can be selected to beno more than 100 million, 10 million, 1 million, 0.1 million, 10,000,1,000, 100 or less. Of course, the plexity can be between one of thelower values and one of the upper values selected from the ranges above.Similar plexity ranges can be achieved using nucleic acid sequencingapproaches such as those known in the art as Next Generation ormassively parallel sequencing.

A variety of commercially available array-based products for detectionof methylation can be used including, for example, the MethylationEPIC™BeadChip™(Illumina, Inc., San Diego, Calif.) which allows interrogationof over 850,000 methylation sites quantitatively across the human genomeat single-nucleotide resolution. Also useful are methylation microarraysavailable from Agilent (Santa Clara, Calif.) and other commercialsuppliers of nucleic acid arrays. The array products can be customizedfor detection of a wide variety of methylation sites in the human genomeor other genomes.

Detection of one or more nucleic acids obtained or generated in atechnique set forth herein can employ a sequencing procedure, such as asequencing-by-synthesis (SBS) technique or other techniques known in theart as massively parallel sequencing or Next Generation sequencing.Briefly, SBS can be initiated by contacting the target nucleic acidswith one or more labeled nucleotides, DNA polymerase, etc. The targetnucleic acid can be derived from a methylation detection technique suchas bisulfate conversion or restriction with a methyl sensitiverestriction endonuclease. Those features where a primer is extendedusing the target nucleic acid as template will incorporate a labelednucleotide that can be detected. Optionally, the labeled nucleotides canfurther include a reversible termination property that terminatesfurther primer extension once a nucleotide has been added to a primer.For example, a nucleotide analog having a reversible terminator moietycan be added to a primer such that subsequent extension cannot occuruntil a deblocking agent is delivered to remove the moiety. Thus, forembodiments that use reversible termination, a deblocking reagent can bedelivered to the flow cell (before or after detection occurs). Washescan be carried out between the various delivery steps. The cycle canthen be repeated n times to extend the primer by n nucleotides, therebydetecting a sequence of length n. Exemplary SBS procedures, fluidicsystems and detection platforms that can be readily adapted for use witha method of the present disclosure are described, for example, inBentley et al., Nature 456:53-59 (2008), WO 04/018497; WO 91/06678; WO07/123744; U.S. Pat. Nos. 7,057,026; 7,329,492; 7,211,414; 7,315,019 or7,405,281, or US Pat. App. Pub. No. 2008/0108082 A1, each of which isincorporated herein by reference.

Other sequencing procedures that detect large numbers of nucleic acidsin parallel can be used, such as pyrosequencing. Pyrosequencing detectsthe release of inorganic pyrophosphate (PPi) as particular nucleotidesare incorporated into a nascent nucleic acid strand (Ronaghi, et al.,Analytical Biochemistry 242(1), 84-9 (1996); Ronaghi, Genome Res. 11(1),3-11 (2001); Ronaghi et al. Science 281(5375), 363 (1998); or U.S. Pat.Nos. 6,210,891; 6,258,568 or 6,274,320, each of which is incorporatedherein by reference). Sequencing-by-ligation reactions are also usefulincluding, for example, those described in Shendure et al. Science309:1728-1732 (2005); or U.S. Pat. No. 5,599,675 or 5,750,341, each ofwhich is incorporated herein by reference. Some embodiments can includesequencing-by-hybridization procedures as described, for example, inBains et al., Journal of Theoretical Biology 135(3), 303-7 (1988);Drmanac et al., Nature Biotechnology 16, 54-58 (1998); Fodor et al.,Science 251(4995), 767-773 (1995); or WO 1989/10977, each of which isincorporated herein by reference. Techniques that use fluorescenceresonance energy transfer (FRET) and/or zeromode waveguides can be usedsuch as those described in Levene et al. Science 299, 682-686 (2003);Lundquist et al. Opt. Lett. 33, 1026-1028 (2008); or Korlach et al.Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the disclosures ofwhich are incorporated herein by reference. Also useful are sequencingtechniques that employ detection of a proton released upon incorporationof a nucleotide into an extension product, such as those commerciallyavailable from Ion Torrent (Guilford, Conn., a Life Technologiessubsidiary) or described in US Pat. App. Pub. Nos. 2009/0026082 A1;2009/0127589 A1; 2010/0137143 A1; or 2010/0282617 A1, each of which isincorporated herein by reference.

Particularly useful sequencing platforms that can be employed includethose commercially available from Illumina, Inc. (San Diego, Calif.)such as the MiSeq™, NextSeq™ or HiSeq™ lines of nucleic acid sequencers;the 454 sequencing systems commercially available from Roche LifeSciences (Basel, Switzerland); the Ion Torrent sequencing systemsavailable from Life Technologies, a subsidiary of Thermo FisherScientific (Waltham, Mass.); or the nanopore sequencing systemscommercially available from Oxford Nanopore (Oxford, England). TheTruSeq™ DNA Methylation Kit is available from Illumina, Inc. and can beused to produce bisulfite sequencing libraries that can be detected onIllumina sequencers. Useful commercial products for preparing nucleicacid samples for detection of methylation on sequencing platforms fromIllumina or other suppliers include, for example, Methylation AnalysisSample Prep Products available from Thermo Fisher Scientific (Waltham,Mass.), Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, AnnArbor, Mich.), EpiMark® Methylated DNA Enrichment Kit available from NewEngland BioLabs (Beverley, Mass.), the Pico Methyl-Seg™ Library Prep Kitavailable from Zymoresearch (Irvine, Calif.), or the Methylamp™Universal Methylated DNA Preparation Kit available from EpiGentek(Farmingdale, N.Y.).

Particular embodiments can include a step of manipulating a nucleic acidsample to enrich for desired nucleic acids. For example, a sample thatis provided for use in a method set forth herein can be subjected totargeted selection of a subset of genomic DNA fragments that include aset of predetermined target CpG sites. Targeted selection can occurprior to or after treating nucleic acids with bisulfite, methylsensitive endonucleases or other reagents used to distinguish methylatedsites from unmethylated sites. A useful targeted selection technique isset forth in Example I, below.

Particular embodiments of the methods set forth herein will evaluateand/or use the coverage determined for each of the sites wheremethylation states have been or will be determined. In some cases thecoverage data is provided to an individual or system that carries outthe method. Alternatively, embodiments of the methods can include one ormore steps for determining coverage at each of the sites.

For embodiments that detect methylation states via a sequencingtechnique, coverage can be considered to describe the average number ofsequencing reads that align to, or “cover,” particular sites (e.g. CpGsites). The Next Generation sequencing coverage level often determineswhether a particular sequence or site can be characterized with acertain degree of confidence. At higher levels of coverage, each site iscovered by a greater number of aligned sequence reads, socharacterizations can be made with a higher degree of confidence. Auseful guide for determining coverage is provided by Illumina TechnicalNote “Estimating Sequencing Coverage” Pub. No. 770-2011-022 (Dec. 1,2014), which is incorporated herein by reference. Similar coveragecriteria can be applied to other detection techniques besides NextGeneration sequencing techniques.

Particular embodiments of the present invention can use coverage that isat least 10×, 30×, 50×, 100×, 1,000×, 5,000×, 10,000× or more at eachsite. Alternatively or additionally, coverage can be at most 10,000×,5,000×, 1,000×, 100×, 50×, 30×, 10× or less. Coverage can be selectedbased on a desired confidence in determining methylation pattern takenin view of the number of sites being evaluated and the quantity of DNAused in the method.

As the number of sites evaluated increases, the confidence in thecharacterization of the sites will also increase. This means a lowercoverage can be acceptable. In particular embodiments the number ofsites evaluated can be at least 10 sites, 100 sites, 500 sites, 1×10³sites, 5×10³ sites, 1×10⁴ sites, 1×10⁵ sites, 1×10⁶ sites or more.Alternatively or additionally, the number of sites evaluated can be atmost 1×10⁶ sites, 1×10⁵ sites, 1×10⁴ sites, 1×10³ sites, 100 sites or 10sites.

The quantity of DNA used in a method set forth herein will depend uponseveral factors such as the sample used and the analytical steps carriedout on the sample. A typical blood draw will provide 30 ng ofcirculating DNA. However, larger or smaller quantities of DNA can beprovided by altering the volume of blood drawn, by using a differenttype of sample (such as those exemplified elsewhere herein) and/orutilizing sample extraction techniques with higher or lower yields.Accordingly, a method of the present invention can be carried out usinga quantity of DNA that is at least 3 ng, 10 ng, 30 ng, 50 ng, 100 ng,500 ng or more. Alternatively or additionally, the quantity of DNA canbe at most 500 ng, 100 ng, 50 ng, 30 ng, 10 ng or 3 ng.

Furthermore, in some embodiments the DNA used in a method for evaluatingmethylation states is a mixture of DNA from a target cell or tissue(e.g. tumor DNA) in a background of DNA from other cells or tissues(e.g. non-tumor DNA). The percent DNA from the target tissue or cell canbe at most 90%, 50%, 25%, 10%, 1%, 0.1%, 0.01% or lower. Alternativelyor additionally, the percent DNA from the target tissue or cell can beat least 0.01%, 0.1%, 1%, 10%, 25%, 50%, 90% or higher.

The above parameters of DNA amount, coverage, number of sites andpercent DNA from the target cell or tissue can be adjusted, for example,within the ranges exemplified above to accommodate a desired confidencelevel in characterizing methylation states for nucleic acids in a methodset forth herein.

Particular embodiments of the methods set forth herein include a step ofproviding methylation states for the plurality of sites in referencegenomic DNA from one or more reference individual organisms. Optionally,a method can include one or more steps for detecting the methylationstates for the plurality of sites in reference genomic DNA from one ormore reference individual organisms. In one aspect, a reference genomicDNA can include, for instance, baseline samples. Any one of the methodsset forth herein for determining methylation states of test DNA can beused to determine methylation states for reference DNA.

Reference genomic DNA, such as baseline samples, that is used in amethod of the present disclosure can be from one or more organism thatis (or are) the same species as the test organism. For example, when thetest organism is an individual human, the reference genomic DNA can befrom a different human individual. In some embodiments, the referencegenomic DNA is from the same individual who provided the test genomicDNA material. For example, the test DNA can be from a tissue suspectedof having a particular condition, whereas the reference DNA is from atissue that is known not to have the condition. In particularembodiments, the test DNA can be from a tumor sample obtained from anindividual whereas the reference DNA is from a normal tissue obtainedfrom the same individual. The tissue or cell types can be the same, butfor the fact that one of the tissue or cell types has a condition thatthe other tissue or cell type does not. Alternatively, different tissueor cell types can be obtained from the individual, one of the tissue orcell types providing test DNA and the other tissue or cell typeproviding reference DNA. A reference genomic DNA can be obtained from ametagenomics sample (e.g. environmental or community sample), forexample, to be used in comparison to a test metagenomics sample.

A test DNA can be derived from one or more test organisms at a differenttime from when a reference DNA, such as baseline samples, is derivedfrom the one or more test organisms. For example, a reference DNA samplecan be obtained from an individual at a time prior to when a disease orcondition is suspected to be present, and then a test DNA sample can beobtained from the individual at a later time when the individual issuspected of having a disease or condition. In such embodiments the testDNA and reference DNA can be obtained from similar tissues, communitiesor cell types or from different tissues, communities or cell types.

In one embodiment, a method of the present disclosure can include a stepof determining, for a plurality of sites (e.g. CpG sites), themethylation difference between test genomic DNA and reference genomicDNA, thereby providing a normalized methylation difference for each site(e.g. CpG site). In particular embodiments the normalized methylationdifference, also referred to as z-score, at a particular site (e.g., CpGsite) is determined according to the formula

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA. Use of the formula for determining methylation differenceis exemplified in Example I, below.

A method of the present disclosure can further include a step ofweighting the normalized methylation difference for each site (e.g., CpGsite) by the coverage at each of the sites (e.g., CpG sites), therebydetermining an aggregate coverage-weighted normalized methylationdifference score. In particular embodiments, an aggregatecoverage-weighted normalized methylation difference score (representedas A) is determined according to the formula

$A = \frac{\sum_{i = 1}^{k}\;{w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}\; w_{i}^{2}}}$wherein w_(i) represents the coverage at site i, and k represents thetotal number of sites. Use of the formula for determining an aggregatecoverage-weighted normalized methylation difference score is exemplifiedin Example I, below.

In particular embodiments, the methods set forth herein can be used toidentify a change in methylation state for a test organism or to monitorsuch changes over time. Accordingly, the present disclosure provides amethod that includes steps of (a) providing a test data set thatincludes (i) methylation states for a plurality of sites from testgenomic DNA from at least one test organism, and (ii) coverage at eachof the sites for detection of the methylation states; (b) providingmethylation states for the plurality of sites in reference genomic DNAfrom one or more reference individual organisms, (c) determining, foreach of the sites, the methylation difference between the test genomicDNA and the reference genomic DNA, thereby providing a normalizedmethylation difference for each site; (d) weighting the normalizedmethylation difference for each site by the coverage at each of thesites, thereby determining an aggregate coverage-weighted normalizedmethylation difference score and (e) repeating steps (a) through (d)using a second test data set that includes (i) methylation states forthe plurality of sites from a second test genomic DNA from theindividual test organism, and (ii) coverage at each of the sites fordetection of the methylation states, and using the same referencegenomic DNA from the at least one reference individual, and (f)determining whether or not a change has occurred in the aggregatecoverage-weighted normalized methylation difference score between thetest genomic DNA and the second test genomic DNA.

Also provided is a method that includes the steps of (a) providing asample containing a mixture of genomic DNA from a plurality of differentcell types from at least one test organism, thereby providing testgenomic DNA; (b) detecting methylation states for a plurality of sitesin the test genomic DNA; (c) determining the coverage at each of thesites for the detecting of the methylation states; (d) providingmethylation states for the plurality of sites in reference genomic DNAfrom at least one reference individual, the at least one test organismand reference individual optionally being the same species; (e)determining, for each of the sites, the methylation difference betweenthe test genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site; (f) weighting thenormalized methylation difference for each site by the coverage at eachof the sites, thereby determining an aggregate coverage-weightednormalized methylation difference score; (g) repeating steps (a) through(f) using a second test genomic DNA provided from a sample comprising amixture of genomic DNA from a plurality of different cell types from theat least one test organism, and using the same reference genomic DNAfrom the at least one reference individual, and (h) determining whetheror not a change has occurred in the aggregate coverage-weightednormalized methylation difference score between the test genomic DNA andthe second test genomic DNA.

In another embodiment, the method is refined to take into considerationthe observed variations in aggregate DNA methylation within a normalpopulation. The test genomic DNA is not compared directly to a referencegenomic DNA; rather, an intermediate step is interposed that includesthe evaluation of a training set of normal genomic DNA samples againstthe reference genomic DNA—referred to in this embodiment as baselinesamples—to assess variation of aggregate DNA methylation within a normalpopulation. This involves calculating “methylation scores” for eachmember of a training set of normal genomic DNA samples, and determiningthe mean and standard deviation of the methylation scores of thetraining set population, thereby yielding information about thedistribution of methylation scores in a normal population. In someembodiments, the number of normal individual organisms providing genomicDNA for the training set is at least 3, at least 5, at least 10, atleast 20, at least 50, or at least 100.

In this embodiment, the method can include a first step of determining,for each CpG site i, the mean methylation level (μ_(i)) and standarddeviation of methylation levels (σ_(i)), observed for a population ofreference genomic DNA. Here, the reference or baseline genomic DNA takesthe form of a population of normal genomic DNA samples. A selectedgenomic DNA can then be compared to the baseline DNA population toevaluate variation in methylation levels. More specifically, methylationlevels at each site i (e.g., CpG site) in a selected genomic DNA can becompared to the population mean, μ_(i), for the baseline samples togenerate a methylation score for the selected genomic DNA. In oneembodiment, the selected genomic DNA is a set of training controls, andin another embodiment, the selected genomic DNA is a test genomic DNA.Methylation levels can be determined by methods that are routine andknown to the skilled person. For example, methylation levels can becalculated as the fraction of ‘C’ bases at a target CpG site out of‘C’+‘U’ bases following the bisulfite treatment, or the fraction of ‘C’bases at a target CpG site out of total ‘C’+‘T’ bases following thebisulfite treatment and subsequent nucleic acid amplification, asdescribed herein.

A methylation score (MS) for a selected genomic DNA can be calculated bydetermining the normalized methylation difference (z-score) at aparticular site i (e.g., CpG site) with reference to a set of baselinesamples, converting the z-score for each site into a probability ofobserving such a z-score or greater (e.g., a one-sided p-value), andcombining the p-values into a final, aggregate methylation score.Optionally, the p-values are weighted. Each of these steps is detailedherein and immediately below.

Methylation scores are initially determined for a training set of normalgenomic DNA samples. First, a normalized methylation difference(z-score) at a particular site i (e.g., CpG site) is determinedaccording to the formula

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in a member of the training set of normal genomic DNA, μ_(i)represents the mean methylation level at site i in the baseline samples,and σ_(i) represents the standard deviation of methylation levels atsite i in the baseline samples.

The z-score for each CpG site i (Z_(i)) is then converted into theprobability of observing such a z-score or greater. In one aspect, theprobability is calculated by converting the z-score into a one-sidedp-value (p_(i)). Probabilities can be calculated assuming a normaldistribution, t-distribution, or binomial distribution. Statisticaltools for such calculations are well known and easily available to aperson of ordinary skill.

Next, a methylation score (MS), an aggregate of the probability of theobserved normalized methylation differences, is determined by combiningthe p-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( p_{i} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, and krepresents the total number of sites. A methylation score is calculatedfor each member of the training set of normal genomic DNA.

Optionally, the p-value at each CpG site can be weighted by multiplyingthe p-value at each CpG site i (p_(i)) with a weighting factor w_(i),where w_(i) can correspond to the significance of the CpG site obtainedfrom a priori knowledge, the depth of coverage associated with the site,or any other ranking method. In this aspect, a methylation score(represented as MS) is determined by combining the weighted p-valuesaccording to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( {w_{i}p_{i}} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of sites, and w_(i) represents the significance, forinstance coverage, of the site i. Use of this formula for determiningweighted methylation scores for a training set of normal genomic DNAsamples is illustrated in Example III.

Statistical analysis of the training set methylation scores is thenperformed. The mean methylation score (μ_(MS)) and standard deviation ofmethylation scores (σ_(MS)) in the training set of normal genomic DNAare calculated. This characterizes the distribution of the methylationscore in a normal population, and can be used to determine whether thegenomic DNA of a test genomic sample has an aberrant methylation level.

The methylation score (MS) of a test genomic DNA is then determined withreference to the baseline samples (as described above for members of thetraining set) and compared to the distribution of the methylation scoresdetermined for the training set of normal genomic DNA.

As described above in connection with the training set, a normalizedmethylation difference (z-score) at a particular site i (e.g., CpG site)is first determined according to the formula

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the baseline samples, and σ_(i) represents thestandard deviation of methylation levels at site i in the baselinesamples.

The z-score for each CpG site i (Z_(i)) is then converted into theprobability of observing such a z-score or greater. In one aspect, theprobability is calculated by converting the z-score into a one-sidedp-value (p_(i)). Probabilities can be calculated assuming a normaldistribution, t-distribution, or binomial distribution. A methylationscore (MS) of the test genomic DNA is determined by combining thep-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( p_{i} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, and krepresents the total number of sites.

Optionally, the p-value at each CpG site can be weighted by multiplyingthe p-value at each CpG site i (p_(i)) with a weight w_(i), where w_(i)can correspond to the significance of the CpG site obtained from apriori knowledge, the depth of coverage associated with the site, or anyother ranking method. A methylation score (MS) of the test genomic DNAis determined by combining the weighted p-values according to the Fisherformula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( {w_{i}p_{i}} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of sites, and w_(i) represents the significance, forinstance coverage, of the site i. Use of this formula for determiningweighted methylation scores for test genomic DNA samples is illustratedin Examples II and III.

Finally, the methylation score of the test genomic DNA is evaluatedagainst the distribution of methylation scores determined for thetraining set population, represented by the mean methylation score(μ_(MS)) and standard deviation of methylation scores (σ_(MS)) for thetraining set of normal genomic DNA. The number of standard deviationsthe methylation score for the test genomic DNA is from the methylationscore mean (μ_(MS)) of the training set of normal genomic DNA isdetermined according to the formula

$Z_{MS} = \frac{{MS} - µ_{MS}}{\sigma_{MS}}$wherein Z_(MS) represents a normalized methylation score difference, MSrepresents the methylation score of the test sample, μ_(MS) representsthe mean methylation score for the training set of normal genomic DNA,and σ_(MS) represents the standard deviation of methylation scores forthe training set of normal genomic DNA. Use of this formula fordetermining normalized methylation score difference is illustrated inExample III. A Z_(MS) value of greater than 1.5, greater than 2, greaterthan 2.5, or greater than 3 standard deviations indicates the testgenomic DNA has an aberrant DNA methylation level. In a preferredembodiment, a Z_(MS) value greater than 3 standard deviations is used asan indication that the test genomic DNA has an aberrant DNA methylationlevel.

In another embodiment, the methods set forth herein can be used toidentify a change in methylation state for a test organism or to monitorsuch changes over time. Accordingly, the present disclosure provides amethod that includes steps of (a) providing methylation states for aplurality of sites (e.g., CpG sites) in baseline genomic DNA from two ormore normal individual organisms; (b) determining, for each of the sites(e.g., CpG sites), the mean methylation level and standard deviation ofmethylation levels for the baseline genomic DNA; (c) providing a testdata set that includes (i) methylation states for the plurality of sites(e.g., CpG sites) from a first test genomic DNA from at least one testorganism, and optionally (ii) coverage at each of the sites (e.g., CpGsites) for detection of the methylation states; (d) determining, foreach of the sites (e.g., CpG sites), the methylation difference betweenthe first test genomic DNA and the baseline genomic DNA, therebyproviding a normalized methylation difference for the first test genomicDNA; (e) converting the normalized methylation difference for the firsttest genomic DNA at each of the sites (e.g., CpG sites) into theprobability of observing such a normalized methylation difference orgreater (e.g., a one-sided p-value), and optionally weighting theprobability of such an event; (f) determining a methylation score forthe first test genomic DNA; (g) repeating steps (c) through (f) using asecond test genomic DNA provided from a sample comprising a mixture ofgenomic DNA from a plurality of different cell types from the at leastone test organism, and using the same baseline genomic DNA; and (h)determining whether or not a change has occurred in the methylationscore between the first test genomic DNA and the second test genomicDNA.

An alternative method of monitoring changes in DNA methylation over timeincludes the steps of (a) providing methylation states for a pluralityof sites (e.g., CpG sites) in baseline genomic DNA from two or morenormal individual organisms; (b) determining, for each of the sites(e.g., CpG sites), the mean methylation level and standard deviation ofmethylation levels for the baseline genomic DNA; (c) providing a mixtureof genomic DNA from a test organism suspected of having a conditionassociated with an aberrant DNA methylation level (e.g., cancer),wherein the mixture comprises genomic DNA from a plurality of differentcell types from the test organism, thereby providing a first testgenomic DNA; (d) detecting methylation states for the plurality of sites(e.g., CpG sites) in the first test genomic DNA, and optionallydetermining the coverage at each of the sites (e.g., CpG sites) for thedetecting of the methylation states; (e) determining, for each of thesites (e.g., CpG sites), the methylation difference between the firsttest genomic DNA and the baseline genomic DNA, thereby providing anormalized methylation difference for the first test genomic DNA; (f)converting the normalized methylation difference for the first testgenomic DNA at each of the sites (e.g., CpG sites) into the probabilityof observing such a normalized methylation difference or greater (e.g.,a one-sided p-value), and optionally weighting the probability of suchan event; (g) determining a methylation score for the first test genomicDNA; (h) repeating steps (c) through (g) using a second test genomic DNAprovided from a sample comprising a mixture of genomic DNA from aplurality of different cell types from the at least one test organism,and using the same baseline genomic DNA; and (i) determining whether ornot a change has occurred in the methylation score between the firsttest genomic DNA and the second test genomic DNA.

First and second test genomic DNA samples (or test data sets) that arecompared in a method set forth herein can be derived from the same typeof cell, community, tissue or fluid, but at different time points.Accordingly, a method set forth herein can be used to identify ormonitor a change that occurs over time. In some embodiments thedifferent time points can occur before, during and/or after a particulartreatment. For example, in the case of monitoring or prognosing cancer,samples can be obtained from an individual before and after initiationof a treatment such as surgery, chemotherapy or radiation therapy.Furthermore multiple samples can be obtained at different time pointsduring treatment. For example the samples can be obtained and evaluatedat time points throughout surgery (e.g. to evaluate whether or notmargins have been cleared of cancerous tissue) or at different timepoints throughout a course of chemotherapy or radiation therapy.Different samples can be obtained from an individual and tested aftertreatment for example to test for relapse and remission.

In a further example, gut metagenomics samples can be obtained beforeand after a treatment (e.g. for a digestive disorder). The methylationstates of the samples can be evaluated and compared to identify changesin the bacterial flora of the gut due to the treatment. The changes inturn can be used to monitor the treatment and determine a prognosis forthe individual being treated.

Any of a variety of sample types set forth herein, or known in the artto contain tumor DNA, can be used in a method for identifying ormonitoring a change in methylation state for an individual. Observedchanges can provide a basis for diagnosis, prognosis, or screening of anindividual with respect to having a particular condition such as cancer.

A method set forth herein can also be used to screen or test a candidatetreatment, for example, in an experimental cell culture, tissue ororganism. Accordingly, a method set forth herein can be used to identifyor monitor a change that occurs over time in a cell culture, tissue ororganism being tested in a clinical or laboratory environment. In someembodiments the different time points can occur before, during and/orafter a particular candidate treatment. For example, samples can beobtained from a test organism before and after initiation of a candidatetreatment such as surgery, chemotherapy or radiation therapy.Furthermore, multiple samples can be obtained at different time pointsduring the candidate treatment. For example the samples can be obtainedand evaluated at time points throughout surgery (e.g. to evaluatewhether or not margins have been cleared of cancerous tissue) or atdifferent time points throughout a course of a candidate chemotherapy orradiation therapy. Different samples can be obtained from a testorganism and tested after a candidate treatment, for example, toevaluate relapse and remission. Control organisms that are not subjectedto the candidate treatment and/or that do not have a particularcondition can also be tested using similar methods. Comparison ofresults between samples subjected to candidate treatments and controlscan be used to determine efficacy and/or safety of a particularcandidate treatment

Any of a variety of sample types set forth herein, or known in the artto contain tumor DNA, can be used in a method for identifying orscreening a candidate treatment. Changes, whether or not being comparedto a particular control, can be used for evaluating efficacy and/orsafety of a particular candidate treatment.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include steps of(a) providing a mixture of genomic DNA from an individual suspected ofhaving the condition (e.g. cancer), wherein the mixture comprisesgenomic DNA from a plurality of different cell types from theindividual, thereby providing test genomic DNA; (b) detectingmethylation states for a plurality of sites (e.g. CpG sites) in the testgenomic DNA; (c) determining the coverage at each of the sites (e.g. CpGsites) for the detecting of the methylation states; (d) providingmethylation states for the plurality of sites (e.g. CpG sites) inreference genomic DNA from at least one reference individual, thereference individual being known to have the condition (e.g. cancer) orknown to not have the condition (e.g. cancer); (e) determining, for eachof the sites (e.g. CpG sites), the methylation difference between thetest genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site (e.g. CpG site); (f)weighting the normalized methylation difference for each site (e.g. CpGsite) by the coverage at each of the sites (e.g. CpG sites), therebydetermining an aggregate coverage-weighted normalized methylationdifference score; and (g) determining that the individual does or doesnot have the condition (e.g. cancer) based on the aggregatecoverage-weighted normalized methylation difference score. In someembodiments the sample is blood and the DNA can, for example, includecell free DNA from the blood.

Also provided is a method for identifying a change in a condition suchas cancer. The method can include steps of (a) providing a mixture ofgenomic DNA from an individual suspected of having the condition (e.g.cancer), wherein the mixture comprises genomic DNA from a plurality ofdifferent cell types from the individual, thereby providing test genomicDNA; (b) detecting methylation states for a plurality of sites (e.g. CpGsites) in the test genomic DNA; (c) determining the coverage at each ofthe sites (e.g. CpG sites) for the detecting of the methylation states;(d) providing methylation states for the plurality of sites (e.g. CpGsites) in reference genomic DNA from at least one reference individual,the reference individual being known to have the condition (e.g. cancer)or known to not have the condition (e.g. cancer); (e) determining, foreach of the sites (e.g. CpG sites), the methylation difference betweenthe test genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site (e.g. CpG site); (f)weighting the normalized methylation difference for each site (e.g. CpGsite) by the coverage at each of the sites (e.g. CpG sites), therebydetermining an aggregate coverage-weighted normalized methylationdifference score; and (g) repeating steps (a) through (f) using a secondmixture of genomic DNA from the individual suspected of having thecondition (e.g. cancer), and using the same reference genomic DNA fromthe at least one reference individual, and (h) determining whether ornot a change has occurred in the aggregate coverage-weighted normalizedmethylation difference score for the second test genomic DNA compared tothe test genomic DNA, thereby determining that a change has or has notoccurred in the condition (e.g. cancer) based on the change in theaggregate coverage-weighted normalized methylation difference score.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include steps of(a) providing methylation states for a plurality of sites (e.g., CpGsites) in baseline genomic DNA from at least one normal individualorganism; (b) determining, for each of the sites (e.g., CpG sites), themean methylation level and standard deviation of methylation levels forthe baseline genomic DNA; (c) providing a training set of normal genomicDNA samples from two or more normal individual organisms that includes(i) methylation states for a plurality of sites (e.g., CpG sites) in thetraining set of normal genomic DNA samples, and optionally (ii) coverageat each of the sites (e.g., CpG sites) for detection of the methylationstates; (d) determining, for each of the sites (e.g., CpG sites), themethylation difference between each normal genomic DNA sample of thetraining set and the baseline genomic DNA, thereby providing anormalized methylation difference for each normal genomic DNA sample ofthe training set at each site (e.g., CpG site); (e) converting thenormalized methylation difference for each normal genomic DNA sample ofthe training set at each site (e.g., CpG site) into the probability ofobserving such a normalized methylation difference or greater (e.g., aone-sided p-value), and optionally weighting the probability of such anevent; (f) determining a methylation score for each normal genomic DNAsample of the training set to obtain training set methylation scores;(g) calculating the mean methylation score and standard deviation of thetraining set methylation scores; (h) providing a mixture of genomic DNAfrom a test organism suspected of having the condition (e.g., cancer),wherein the mixture comprises genomic DNA from a plurality of differentcell types from the test organism, thereby providing test genomic DNA;(i) detecting methylation states for the plurality of sites (e.g., CpGsites) in the test genomic DNA, and optionally determining the coverageat each of the sites (e.g., CpG sites) for the detecting of themethylation states; (j) determining, for each of the sites (e.g., CpGsites), the methylation difference between the test genomic DNA and thebaseline genomic DNA, thereby providing a normalized methylationdifference for the test genomic DNA; (k) converting the normalizedmethylation difference for the test genomic DNA at each of the sites(e.g., CpG sites) into the probability of observing such a normalizedmethylation difference or greater (e.g., a one-sided p-value), andoptionally weighting the probability of such an event; (1) determining amethylation score for the test genomic DNA; and (m) comparing themethylation score of the test genomic DNA to the mean methylation scoreand standard deviation of methylation scores in the training set ofnormal genomic DNA to determine the number of standard deviations themethylation score of the test genomic DNA is from the distribution ofmethylation scores in the training set of normal genomic DNA. In theevent the number of standard deviations exceeds a predeterminedthreshold value (e.g., 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, etc.),the test sample is considered to have an aberrant DNA methylation level.

Optionally, the sites from the test genomic DNA are derived from aplurality of different cell types from the individual test organism, andas a further option, the cell type from which each of the sites (e.g.,CpG sites) is derived is unknown. In a further optional embodiment, theindividual test organism and the one or more baseline individualorganisms, training individual organisms, or a combination thereof arethe same species. In some embodiments, the mixture of genomic DNA froman individual suspected of having the condition is blood and the DNAcan, for example, include cell-free DNA (cfDNA) or circulating tumor DNA(ctDNA) from the blood.

Also provided herein is a method for identifying a change in a conditionsuch as cancer over time. The method can include steps of (a) providingmethylation states for a plurality of sites (e.g., CpG sites) inbaseline genomic DNA from at least one normal individual organism; (b)determining, for each of the sites (e.g., CpG sites), the meanmethylation level and standard deviation of methylation levels for thebaseline genomic DNA; (c) providing a first mixture of genomic DNA froma test organism suspected of having the condition (e.g., cancer),wherein the first mixture comprises genomic DNA from a plurality ofdifferent cell types from the test organism, thereby providing a firsttest genomic DNA; (d) detecting methylation states for the plurality ofsites (e.g., CpG sites) in the first test genomic DNA, and optionallydetermining the coverage at each of the sites (e.g., CpG sites) for thedetecting of the methylation states; (e) determining, for each of thesites (e.g., CpG sites), the methylation difference between the firsttest genomic DNA and the baseline genomic DNA, thereby providing anormalized methylation difference for the first test genomic DNA; (f)converting the normalized methylation difference for the first testgenomic DNA at each of the sites (e.g., CpG sites) into the probabilityof observing such a normalized methylation difference or greater (e.g.,a one-sided p-value), and optionally weighting the probability of suchan event; (g) determining a methylation score for the first test genomicDNA; (h) repeating steps (c) through (g) using a second mixture ofgenomic DNA from the test organism suspected of having the condition(e.g., cancer), wherein the second mixture comprises a second testgenomic DNA, and (i) determining whether or not a change has occurred inthe methylation score for the second test genomic DNA compared to thefirst test genomic DNA, thereby determining that a change has or has notoccurred in the condition (e.g., cancer) based on the change in themethylation score.

Methylation states determined using methods set forth herein can be usedfor molecular classification and prediction of cancers using criteriathat have been developed for gene expression and other genomic data(see, for example, Golub et al. (1999) Molecular classification ofcancer: class discovery and class prediction by gene expressionmonitoring. Science, 286, 531-537). Other classification systems thatcan be used include those that have been developed for correlatingglobal changes in methylation pattern to molecular classification inbreast cancer (see, for example, Huang et al. (1999) Methylationprofiling of CpG sites in human breast cancer cells. Hum Mol Genet, 8,459-470), or those developed for correlating methylation patterns intumor suppressor genes (for example, p16, a cyclin-dependent kinaseinhibitor) in certain human cancer types (see, for example, Herman etal. (1995) Inactivation of the CDKN2/p16/MTS1 gene is frequentlyassociated with aberrant DNA methylation in all common human cancers.Cancer Res, 55, 4525-4530.; Otterson et al. (1995) CDKN2 gene silencingin lung cancer by DNA hypermethylation and kinetics of p16INK4 proteininduction by 5-aza 2′deoxycytidine. Oncogene, 11, 1211-1216). The abovereferences are incorporated herein by reference.

In some applications of the methylation analysis methods set forthherein, a model can be developed to predict the disease type withoutprior pathological diagnosis. Thus, in some embodiments, the methods setforth herein are used to determine methylation patterns in staged tumorsamples relative to matched normal tissues from the same patient. Thedetermined differences in methylation pattern between the tumor andnormal tissues can be used to build a model to predict, diagnose ormonitor cancer. For example, methylation patterns determined for a testsample can be compared to a methylation pattern from a known normaland/or from a known tumor, and a diagnosis can be made based on thedegree of similarity of the test sample to one or both of thesereferences.

In addition, the methods set forth herein can facilitate identification,classification and prognostic evaluation of tumors. This information canin turn be used to identify subgroups of tumors with related properties.Such classification has been useful in identifying the causes of varioustypes of cancer and in predicting their clinical behavior.

In particular embodiments of the present methods, cancers are predicted,detected, identified, classified, or monitored from cell free DNA ofcancer patients. For example, the determination of a methylation patternfrom a plasma sample can be used to screen for cancer. When themethylation pattern of the plasma sample is aberrant compared with ahealthy reference, cancer may be suspected. Then further confirmationand assessment of the type of cancer or tissue origin of the cancer canbe performed by determining the plasma profile of methylation atdifferent genomic loci or by plasma genomic analysis to detecttumor-associated copy number aberrations, chromosomal translocations andsingle nucleotide variants. Alternatively, radiological and imaginginvestigations (e.g. computed tomography, magnetic resonance imaging,positron emission tomography) or endoscopy (e.g. upper gastrointestinalendoscopy or colonoscopy) can be used to further investigate individualswho were suspected of having cancer based on the plasma methylationlevel analysis.

In one aspect of the present invention, provided herein is a method forusing methylation levels to identify or classify a specific type ofcancer in a test organism, preferably a mammalian organism, morepreferably a human. In this aspect, methylation levels of a test genomicDNA are evaluated, for subsets of preselected methylation sitesassociated with known cancer types, herein referred to as“hypermethylated” sites, and then ranked from lowest to highest. Thecancer type corresponding to the highest average methylation level isconsidered to be associated with the test genomic DNA, i.e. the cancertype is deemed to be present in the test organism.

As a starting point, the method can include identifying specific cancersthat can be used as a cancer type in the identification orclassification algorithm according to this aspect of the invention. Acancer type is a cancer, e.g., breast invasive carcinoma, colonadenocarcinoma, lung adenocarcinoma, and others, that can be used as amember of a panel of specific cancers to determine whether a testorganism has a specific type of cancer.

Determining whether a cancer can be used as a cancer type in the presentmethod includes obtaining genomic DNA sequence data from clinicalsamples. Genomic DNA sequence data useful herein is readily availablefrom known databases that characterize genomic and epigenomicchanges—such as changes in methylation state—in different types ofcancers. The greater the number of clinical samples of a cancer in adatabase, the more likely the cancer can be used as a cancer type. Acancer type suitable for the present method may be defined using genomicDNA sequence data from at least 10, at least 15, at least 20, at least25, at least 30, at least 40, at least 50, at least 75, or at least 100clinical samples of a specific cancer.

Once a panel of suitable cancer type has been defined, a list ofso-called “hypermethylated” sites specific for each cancer type isassembled. In some embodiments, useful methylation sites that can beevaluated for methylation state include the selected CpG sites of thePan Cancer Panel set forth in Table I (the listed methylation sites arefrom Genome Build 37) and/or set forth in Table II (the listedmethylation sites are from Genome Build 37). In other embodiments,useful methylation sites that can be evaluated for methylation stateinclude those present in The Cancer Genome Atlas (see, for example,Cancer Genome Atlas Research Network et al., Nature Genetics45:1113-1120 (2013)), the CpG sites used to identify or monitorcolorectal cancer described in Worthley et al., Oncogene 29, 1653-1662(2010), and methylation markers for detection of ovarian cancer setforth in US Pat. App. Pub. No. 2008/0166728 A1, among others. All of thecited documents are incorporated herein by reference in theirentireties. All or a subset of the sites set forth herein or listed in areference herein can be used in the identification or classificationmethod set forth herein. For example, at least 100, 1×10³, 1×10⁴, 1×10⁵,1×10⁶, or more of the methylation sites can be used as a starting point.In some embodiments, the entire methylome (i.e. the full set ofmethylation sites in a test organism's genome) may be used to selecthypermethylated sites suitable for the present method.

TABLE I Pan Cancer Panel cg00006948 cg00012992 cg00019137 cg00021108cg00026375 cg00027037 cg00039627 cg00041084 cg00056676 cg00059034cg00073771 cg00073780 cg00079563 cg00081574 cg00091964 cg00107016cg00114393 cg00114963 cg00115040 cg00117463 cg00121634 cg00121640cg00124160 cg00128353 cg00132108 cg00134776 cg00136947 cg00139244cg00141174 cg00143220 cg00145489 cg00151810 cg00155423 cg00157987cg00158254 cg00164196 cg00168514 cg00169305 cg00183340 cg00196372cg00202702 cg00205263 cg00207389 cg00208931 cg00210994 cg00214530cg00220517 cg00221969 cg00233079 cg00235260 cg00235337 cg00245538cg00246817 cg00251610 cg00254802 cg00259618 cg00262031 cg00264591cg00266918 cg00267325 cg00275232 cg00280758 cg00281977 cg00283576cg00286984 cg00288050 cg00289081 cg00291351 cg00302521 cg00303548cg00303672 cg00310855 cg00311654 cg00312474 cg00318608 cg00322319cg00325599 cg00338893 cg00341980 cg00344260 cg00346326 cg00350003cg00351011 cg00352349 cg00353340 cg00358220 cg00365470 cg00367047cg00370303 cg00371920 cg00372486 cg00381697 cg00389976 cg00395632cg00397851 cg00401880 cg00404838 cg00405843 cg00407729 cg00408906cg00414171 cg00414398 cg00415978 cg00419564 cg00440043 cg00442814cg00447632 cg00449821 cg00450312 cg00456894 cg00466108 cg00466364cg00470794 cg00471966 cg00474209 cg00476317 cg00480136 cg00483446cg00485296 cg00485849 cg00486611 cg00486627 cg00487870 cg00488787cg00489861 cg00495503 cg00498155 cg00499289 cg00503704 cg00504703cg00507727 cg00512374 cg00525503 cg00527440 cg00533620 cg00549463cg00549910 cg00551736 cg00552973 cg00559018 cg00560547 cg00562243cg00567696 cg00574530 cg00576301 cg00577109 cg00581731 cg00582881cg00583303 cg00586537 cg00588720 cg00591844 cg00593962 cg00594560cg00594866 cg00598730 cg00603617 cg00607526 cg00611485 cg00613753cg00614641 cg00616965 cg00618725 cg00622677 cg00626110 cg00633736cg00639886 cg00642494 cg00643111 cg00650006 cg00651829 cg00656387cg00656411 cg00658626 cg00659495 cg00661753 cg00663739 cg00673557cg00679738 cg00683895 cg00687122 cg00691999 cg00692763 cg00695712cg00697033 cg00702008 cg00704633 cg00708380 cg00709515 cg00712044cg00713925 cg00719143 cg00720475 cg00735962 cg00741789 cg00744920cg00745606 cg00747619 cg00751156 cg00752016 cg00752376 cg00755836cg00757182 cg00758584 cg00761787 cg00769520 cg00769843 cg00773459cg00780574 cg00790649 cg00793280 cg00796360 cg00796793 cg00797346cg00800993 cg00803088 cg00803816 cg00808740 cg00809888 cg00813603cg00815093 cg00818693 cg00819163 cg00821073 cg00824141 cg00828602cg00834400 cg00835429 cg00838874 cg00843352 cg00845942 cg00846483cg00850971 cg00868383 cg00873850 cg00877887 cg00879003 cg00879447cg00880074 cg00884221 cg00887511 cg00894435 cg00896540 cg00899483cg00901765 cg00906644 cg00907211 cg00918541 cg00919118 cg00929855cg00939301 cg00940278 cg00944142 cg00948275 cg00953355 cg00953777cg00954566 cg00964103 cg00965391 cg00966974 cg00969047 cg00969787cg00971804 cg00976157 cg00977805 cg00979348 cg00983637 cg00984694cg00992385 cg00994693 cg00996262 cg00996986 cg00999950 cg01009697cg01012280 cg01015652 cg01016553 cg01016662 cg01019028 cg01024009cg01025398 cg01029840 cg01033463 cg01035198 cg01043524 cg01045612cg01052477 cg01054622 cg01060026 cg01060059 cg01069256 cg01070355cg01070794 cg01076997 cg01078147 cg01079098 cg01079658 cg01083689cg01093319 cg01097611 cg01097881 cg01097964 cg01098237 cg01099231cg01099875 cg01101742 cg01105385 cg01107801 cg01108392 cg01112082cg01112965 cg01126855 cg01141237 cg01142386 cg01143579 cg01143804cg01145317 cg01151699 cg01152936 cg01153132 cg01156948 cg01157070cg01160085 cg01175682 cg01179851 cg01181350 cg01183053 cg01184522cg01186777 cg01187533 cg01191114 cg01196517 cg01204271 cg01211349cg01215936 cg01216370 cg01224730 cg01226806 cg01227006 cg01228636cg01236148 cg01244034 cg01244650 cg01245656 cg01246835 cg01247426cg01250961 cg01254575 cg01256674 cg01258587 cg01259619 cg01263075cg01263292 cg01263716 cg01267522 cg01268683 cg01269344 cg01269537cg01270246 cg01274625 cg01275523 cg01277490 cg01280202 cg01284881cg01289643 cg01294263 cg01300341 cg01307939 cg01308268 cg01310019cg01310600 cg01313313 cg01313518 cg01315063 cg01316109 cg01327552cg01333350 cg01335685 cg01335781 cg01339444 cg01340952 cg01341170cg01363902 cg01367393 cg01369082 cg01370014 cg01370063 cg01370181cg01372071 cg01373292 cg01377006 cg01380710 cg01384163 cg01385795cg01387945 cg01394093 cg01398050 cg01402994 cg01404988 cg01406536cg01409659 cg01414358 cg01418667 cg01419567 cg01421405 cg01424281cg01429635 cg01431908 cg01434487 cg01442844 cg01445942 cg01446203cg01450204 cg01451328 cg01453694 cg01458686 cg01460805 cg01462053cg01464969 cg01466678 cg01479818 cg01483681 cg01485010 cg01486752cg01492246 cg01498231 cg01499217 cg01504555 cg01505590 cg01505767cg01506130 cg01506492 cg01507044 cg01507046 cg01511379 cg01514859cg01518631 cg01522456 cg01532206 cg01544751 cg01548300 cg01549404cg01555604 cg01556502 cg01558040 cg01562471 cg01563031 cg01564068cg01565690 cg01566304 cg01573616 cg01583034 cg01583969 cg01587630cg01588748 cg01593751 cg01597066 cg01601746 cg01606085 cg01606998cg01607295 cg01611886 cg01612478 cg01613306 cg01616178 cg01616474cg01618114 cg01618829 cg01632300 cg01637011 cg01637551 cg01641096cg01645113 cg01646610 cg01646639 cg01649597 cg01653005 cg01656221cg01657408 cg01665212 cg01666600 cg01667646 cg01667837 cg01677561cg01678172 cg01678714 cg01682021 cg01692340 cg01699584 cg01706029cg01706789 cg01719157 cg01719793 cg01722423 cg01722994 cg01725608cg01740172 cg01740424 cg01742897 cg01743617 cg01743962 cg01754037cg01754155 cg01757209 cg01763173 cg01764954 cg01772385 cg01775260cg01778450 cg01780109 cg01782227 cg01785417 cg01791407 cg01794473cg01814098 cg01814945 cg01822124 cg01831896 cg01833212 cg01834210cg01839688 cg01844352 cg01844539 cg01851208 cg01857475 cg01861574cg01862172 cg01869273 cg01869826 cg01871428 cg01873234 cg01876194cg01885963 cg01890984 cg01893041 cg01908537 cg01911237 cg01912921cg01922936 cg01940943 cg01947949 cg01948390 cg01949798 cg01950665cg01952683 cg01955962 cg01962509 cg01962510 cg01966612 cg01967288cg01969058 cg01970519 cg01970575 cg01970784 cg01978544 cg01981187cg01984858 cg01992107 cg01995821 cg01998047 cg02002584 cg02005505cg02012576 cg02012731 cg02014107 cg02020882 cg02028389 cg02030493cg02033141 cg02034497 cg02048890 cg02052895 cg02059348 cg02062409cg02065704 cg02072885 cg02078870 cg02079933 cg02086858 cg02096492cg02101625 cg02110141 cg02114924 cg02115050 cg02115539 cg02120463cg02120582 cg02126753 cg02128087 cg02132163 cg02132470 cg02134353cg02136132 cg02138756 cg02144298 cg02151754 cg02162906 cg02169113cg02169734 cg02172579 cg02174225 cg02180498 cg02196227 cg02200939cg02202600 cg02202980 cg02205739 cg02215070 cg02219071 cg02221750cg02221866 cg02229097 cg02229993 cg02231729 cg02232273 cg02233216cg02236651 cg02241397 cg02247561 cg02248320 cg02250071 cg02251557cg02257090 cg02257750 cg02260353 cg02265056 cg02266348 cg02270183cg02273903 cg02277383 cg02282626 cg02284150 cg02284587 cg02285922cg02286547 cg02290238 cg02293228 cg02298956 cg02303897 cg02304863cg02306127 cg02307033 cg02307605 cg02310733 cg02315971 cg02316216cg02328010 cg02330121 cg02330494 cg02331143 cg02340915 cg02344926cg02346970 cg02352687 cg02352723 cg02353937 cg02357043 cg02362467cg02362970 cg02369195 cg02384967 cg02388709 cg02394263 cg02398045cg02398612 cg02399645 cg02400449 cg02405503 cg02406285 cg02407785cg02408333 cg02409108 cg02424378 cg02425263 cg02430347 cg02435495cg02445664 cg02447380 cg02448922 cg02454595 cg02460997 cg02461665cg02470625 cg02472291 cg02474799 cg02481778 cg02483484 cg02485200cg02486351 cg02487654 cg02492778 cg02492791 cg02503395 cg02506053cg02510164 cg02511156 cg02513017 cg02513409 cg02527199 cg02532538cg02537163 cg02539714 cg02547025 cg02551396 cg02552311 cg02554246cg02557406 cg02557432 cg02558627 cg02560717 cg02565702 cg02567082cg02574526 cg02583334 cg02584489 cg02593205 cg02595750 cg02596331cg02602699 cg02604121 cg02608019 cg02617469 cg02617655 cg02618553cg02620228 cg02620694 cg02622885 cg02624855 cg02627531 cg02628801cg02630553 cg02633073 cg02636497 cg02637978 cg02638057 cg02639285cg02639993 cg02643218 cg02649987 cg02651961 cg02654360 cg02655739cg02657292 cg02659086 cg02659794 cg02660823 cg02664993 cg02665570cg02666434 cg02666504 cg02669964 cg02671646 cg02673256 cg02687055cg02688760 cg02690609 cg02692405 cg02701278 cg02708401 cg02711801cg02712555 cg02713068 cg02713266 cg02716516 cg02717437 cg02723311cg02725055 cg02737782 cg02738081 cg02739708 cg02750883 cg02757194cg02759151 cg02761345 cg02767177 cg02767539 cg02767960 cg02770946cg02776035 cg02776314 cg02783889 cg02784301 cg02787320 cg02795700cg02796773 cg02797548 cg02816363 cg02819231 cg02820717 cg02831090cg02835214 cg02836020 cg02836541 cg02838118 cg02841941 cg02842629cg02850812 cg02854695 cg02855409 cg02862354 cg02862904 cg02866454cg02867728 cg02871940 cg02873868 cg02874371 cg02876237 cg02877791cg02882044 cg02883595 cg02885694 cg02886549 cg02888838 cg02888906cg02891774 cg02892350 cg02892595 cg02892898 cg02893482 cg02899206cg02905065 cg02906238 cg02915746 cg02916964 cg02917917 cg02918146cg02918224 cg02921003 cg02921269 cg02921583 cg02927655 cg02929073cg02930242 cg02933119 cg02934500 cg02938682 cg02939019 cg02942594cg02945007 cg02951059 cg02951206 cg02951568 cg02952978 cg02954735cg02955219 cg02958718 cg02962318 cg02968116 cg02971481 cg02977761cg02978421 cg02980693 cg02982690 cg02982793 cg02983203 cg02993259cg02995055 cg03001116 cg03001832 cg03003689 cg03004714 cg03015433cg03016097 cg03016991 cg03024517 cg03024536 cg03031959 cg03038003cg03040279 cg03052869 cg03053575 cg03054643 cg03057213 cg03059112cg03060802 cg03065202 cg03071143 cg03081134 cg03082580 cg03088791cg03089869 cg03096401 cg03098937 cg03100040 cg03103035 cg03103770cg03108238 cg03113285 cg03116642 cg03122735 cg03125329 cg03132532cg03141007 cg03141069 cg03141620 cg03143697 cg03143742 cg03147990cg03148427 cg03153658 cg03157531 cg03159947 cg03165343 cg03168582cg03169767 cg03170611 cg03175305 cg03181829 cg03188118 cg03189990cg03191830 cg03192963 cg03202738 cg03209812 cg03210277 cg03217173cg03223126 cg03223733 cg03238298 cg03255556 cg03257575 cg03259494cg03265944 cg03273700 cg03279535 cg03288419 cg03290040 cg03297593cg03307911 cg03309367 cg03309726 cg03311339 cg03311459 cg03313212cg03315058 cg03321003 cg03324578 cg03328664 cg03332113 cg03334130cg03334540 cg03342084 cg03342530 cg03347559 cg03347944 cg03352181cg03356115 cg03356760 cg03364193 cg03365985 cg03366439 cg03366925cg03369344 cg03369477 cg03382304 cg03383295 cg03386480 cg03387066cg03391040 cg03392673 cg03393966 cg03397750 cg03405315 cg03410359cg03410436 cg03411979 cg03415695 cg03424727 cg03425504 cg03427543cg03430348 cg03430923 cg03431079 cg03434847 cg03437204 cg03443751cg03446195 cg03450370 cg03455458 cg03462055 cg03465206 cg03467027cg03468349 cg03470396 cg03472672 cg03476291 cg03479657 cg03485262cg03495059 cg03496713 cg03502284 cg03506609 cg03506979 cg03513246cg03521258 cg03524308 cg03525011 cg03526256 cg03532274 cg03535663cg03536983 cg03537779 cg03544918 cg03545133 cg03547745 cg03552151cg03552992 cg03554817 cg03556393 cg03556653 cg03559229 cg03562044cg03577052 cg03586803 cg03598499 cg03603214 cg03603951 cg03604840cg03607117 cg03607359 cg03608167 cg03609148 cg03609308 cg03611007cg03612722 cg03613077 cg03615913 cg03621100 cg03626278 cg03626734cg03631864 cg03638874 cg03648780 cg03650154 cg03662422 cg03663746cg03668475 cg03673687 cg03675739 cg03681341 cg03691812 cg03694261cg03695666 cg03699307 cg03701001 cg03701745 cg03704912 cg03707948cg03710481 cg03711182 cg03712038 cg03717315 cg03719634 cg03721976cg03722871 cg03735847 cg03738134 cg03741406 cg03751813 cg03753681cg03753849 cg03754311 cg03756448 cg03757145 cg03757871 cg03767822cg03768777 cg03770147 cg03771448 cg03774026 cg03776464 cg03778788cg03780545 cg03785281 cg03786924 cg03801902 cg03802907 cg03806238cg03808158 cg03809147 cg03817671 cg03817911 cg03818920 cg03818977cg03818992 cg03822259 cg03824617 cg03836414 cg03839661 cg03843031cg03846951 cg03860020 cg03860859 cg03861105 cg03863616 cg03871549cg03880509 cg03884587 cg03884792 cg03894174 cg03896436 cg03899372cg03900646 cg03901784 cg03909781 cg03911494 cg03920233 cg03921179cg03921416 cg03921599 cg03927893 cg03929741 cg03930088 cg03938598cg03940620 cg03947464 cg03954442 cg03961800 cg03974423 cg03978375cg03979582 cg03980991 cg03985136 cg03986989 cg03991848 cg03998871cg04005725 cg04008429 cg04010471 cg04011182 cg04012592 cg04012924cg04017769 cg04022379 cg04027074 cg04028634 cg04044297 cg04046599cg04049102 cg04051458 cg04054012 cg04057016 cg04058593 cg04072843cg04073970 cg04076682 cg04083712 cg04083751 cg04083753 cg04085025cg04089426 cg04092682 cg04095732 cg04099652 cg04106782 cg04110544cg04112845 cg04125371 cg04133572 cg04134305 cg04140862 cg04141379cg04145287 cg04148762 cg04156369 cg04159901 cg04167903 cg04171539cg04171853 cg04175417 cg04176674 cg04180299 cg04188397 cg04197823cg04199931 cg04199943 cg04206517 cg04209650 cg04216289 cg04219247cg04219613 cg04220088 cg04220579 cg04227789 cg04232325 cg04234680cg04235146 cg04243181 cg04245373 cg04258811 cg04261877 cg04262938cg04269188 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cg26635845 cg26640467 cg26642510 cg26644059 cg26647219cg26647312 cg26649005 cg26652447 cg26653400 cg26654790 cg26654994cg26660414 cg26660801 cg26664090 cg26664797 cg26667720 cg26672104cg26672688 cg26673436 cg26674800 cg26675876 cg26678852 cg26680047cg26680520 cg26681211 cg26685735 cg26691604 cg26705425 cg26705553cg26708548 cg26708817 cg26709356 cg26709950 cg26713507 cg26715571cg26717983 cg26717995 cg26720389 cg26725153 cg26725274 cg26729197cg26731119 cg26732691 cg26744375 cg26745332 cg26745551 cg26745770cg26754826 cg26756506 cg26757053 cg26768712 cg26770917 cg26774079cg26776069 cg26781150 cg26784596 cg26785250 cg26785617 cg26788916cg26789332 cg26789732 cg26789869 cg26790372 cg26791905 cg26792080cg26792116 cg26792755 cg26795730 cg26796283 cg26797372 cg26797585cg26798879 cg26799802 cg26800162 cg26800371 cg26802053 cg26802291cg26815291 cg26820118 cg26822161 cg26823584 cg26824467 cg26825751cg26825934 cg26826871 cg26831241 cg26832509 cg26834887 cg26838995cg26839117 cg26840598 cg26843430 cg26847805 cg26851796 cg26853987cg26857911 cg26859900 cg26861593 cg26862527 cg26864834 cg26870192cg26872138 cg26873311 cg26877715 cg26878816 cg26881527 cg26886307cg26886972 cg26887632 cg26891924 cg26892444 cg26896668 cg26899113cg26904700 cg26904914 cg26908611 cg26908755 cg26909954 cg26910092cg26912242 cg26912984 cg26915774 cg26917673 cg26918442 cg26919818cg26922444 cg26923908 cg26924294 cg26928603 cg26929012 cg26929348cg26932552 cg26933063 cg26942943 cg26943708 cg26948603 cg26953462cg26954625 cg26955196 cg26957677 cg26958597 cg26958806 cg26963029cg26966630 cg26966707 cg26967167 cg26967579 cg26968387 cg26970841cg26970847 cg26972058 cg26973488 cg26982364 cg26984626 cg26986911cg26987855 cg26988146 cg26988215 cg26988406 cg26988692 cg26988895cg26990660 cg26992213 cg26995744 cg26995992 cg26998900 cg26999505cg27002185 cg27003849 cg27004639 cg27009208 cg27011620 cg27014927cg27015174 cg27016990 cg27018185 cg27023953 cg27025752 cg27028168cg27029179 cg27030612 cg27031632 cg27032142 cg27036581 cg27037103cg27040423 cg27041875 cg27042000 cg27046492 cg27051954 cg27052403cg27056599 cg27060622 cg27061115 cg27062573 cg27062617 cg27063138cg27064266 cg27066284 cg27068170 cg27068490 cg27070869 cg27072012cg27072749 cg27073079 cg27078812 cg27080194 cg27080211 cg27082486cg27084026 cg27084903 cg27085904 cg27086020 cg27086773 cg27087057cg27088830 cg27090492 cg27093143 cg27099166 cg27099274 cg27099500cg27101125 cg27108362 cg27109877 cg27111970 cg27112897 cg27116061cg27116819 cg27116888 cg27117639 cg27121584 cg27123533 cg27123665cg27125972 cg27128984 cg27133864 cg27134223 cg27136241 cg27138195cg27139933 cg27140220 cg27143938 cg27147871 cg27151812 cg27154391cg27162464 cg27164044 cg27166527 cg27173322 cg27175294 cg27178940cg27180880 cg27182172 cg27192597 cg27194921 cg27197380 cg27199872cg27204739 cg27209110 cg27212234 cg27213352 cg27220968 cg27222172cg27223727 cg27225570 cg27229407 cg27230038 cg27233989 cg27240775cg27242945 cg27243389 cg27244585 cg27249178 cg27250841 cg27253670cg27254295 cg27257566 cg27258025 cg27258933 cg27266382 cg27269940cg27275523 cg27276115 cg27279904 cg27286572 cg27293549 cg27295197cg27295781 cg27299538 cg27303430 cg27304043 cg27304516 cg27306119cg27307206 cg27308557 cg27310163 cg27311227 cg27313572 cg27315243cg27319192 cg27323154 cg27326642 cg27326687 cg27326823 cg27327588cg27331401 cg27333693 cg27335720 cg27336178 cg27338377 cg27339550cg27340350 cg27346528 cg27347140 cg27351780 cg27352765 cg27355739cg27358097 cg27359731 cg27360003 cg27361727 cg27362103 cg27362222cg27363741 cg27364874 cg27371984 cg27372898 cg27375072 cg27380218cg27380819 cg27382164 cg27383277 cg27386292 cg27388983 cg27391396cg27392850 cg27395666 cg27395939 cg27405644 cg27405960 cg27410952cg27412987 cg27413430 cg27417717 cg27418586 cg27420520 cg27421939cg27422496 cg27425719 cg27425996 cg27430961 cg27432847 cg27433451cg27434954 cg27434993 cg27436264 cg27437806 cg27443310 cg27445400cg27447053 cg27447689 cg27447868 cg27448015 cg27449131 cg27449352cg27451672 cg27453606 cg27457941 cg27462418 cg27464311 cg27465275cg27465717 cg27466237 cg27469783 cg27475076 cg27476262 cg27476576cg27478700 cg27479418 cg27482605 cg27486637 cg27487839 cg27492749cg27496650 cg27502066 cg27504805 cg27506082 cg27506462 cg27507700cg27511208 cg27511255 cg27517702 cg27519691 cg27525037 cg27530629cg27533019 cg27534281 cg27536453 cg27538026 cg27545919 cg27546065cg27546736 cg27546949 cg27549834 cg27552287 cg27555382 cg27558479cg27558594 cg27559724 cg27560922 cg27564875 cg27565366 cg27565555cg27569040 cg27569822 cg27576755 cg27577527 cg27579532 cg27584828cg27586581 cg27586588 cg27591375 cg27594756 cg27597110 cg27599958cg27604626 cg27606464 cg27607338 cg27612364 cg27619163 cg27627570cg27629771 cg27632050 cg27633530 cg27635394 cg27636310 cg27637930cg27637948 cg27638597 cg27641141 cg27646469 cg27647384 cg27650778cg27654641 cg27655921 cg27658416 cg27659841 cg27665925

TABLE II CRC Panel cg20295442 cg20463526 cg26122980 cg09822538cg26212877 cg20560075 cg16601494 cg27555582 cg10864878 cg03384825cg22538054 cg11017065 cg13405887 cg09022943 cg19651223 cg16476975cg17228900 cg05527869 cg01051310 cg12348588 cg20329153 cg02970696cg02259324 cg15778437 cg07703462 cg25088758 cg04537567 cg17222500cg15490715 cg20219457 cg16300300 cg11979589 cg05051043 cg12940822cg01563031 cg22065614 cg13024709 cg15467646 cg18120376 cg19939997cg19824907 cg18683604 cg07188591 cg14175690 cg15658945 cg01938650cg20556517 cg13726682 cg06952671 cg06913330 cg22834653 cg05046525cg17035091 cg03419885 cg10512745 cg03976877 cg09667303 cg03401096cg01883425 cg17287235 cg02173749 cg11501438 cg04897742 cg14236735cg11855526 cg17768491 cg09498146 cg25730685 cg10236452 cg04184836cg04198308 cg10362542 cg14348439 cg17470837 cg11281641 cg17698295cg11666087 cg18587340 cg25798987 cg07976064 cg13101087 cg09975620cg23217126 cg10457056 cg22623967 cg08430489 cg09740671 cg02043600cg24392818 cg25975712 cg03225817 cg26820055 cg18638914 cg00421139cg21672843 cg15384598 cg18884037 cg01419567 cg13554086 cg07974511cg07700514 cg23272632 cg16993043 cg01394819 cg23300368 cg16556906cg12816961 cg01947130 cg02604524 cg24487076 cg06528267 cg21938148cg03356747 cg16334314 cg20864608 cg03640756 cg13223402 cg04125371cg05209770 cg00843236 cg00662647 cg20079899 cg17029156 cg08558397cg08452658 cg01261798 cg04904331 cg03571927 cg08189989 cg15699267cg04790084 cg10058779 cg16918905 cg27200446 cg15015920 cg22879515cg16638385 cg02511156 cg02455397 cg27442308 cg20631014 cg00817367cg22474464 cg09802835 cg22871668 cg19875368 cg14098681 cg15779837cg08354093 cg14794428 cg15825786 cg12417685 cg04272632 cg21039708cg24033330 cg14485004 cg13690864 cg20012008 cg03133266 cg26274580cg20686234 cg03957481 cg04718428 cg14473327 cg15207742 cg12907379cg12042659 cg05374412 cg16676492 cg03755177 cg21314480 cg16230141cg10453425 cg26495865 cg05522774 cg10293925 cg10002178 cg02583633cg02539855 cg20443778 cg25012919 cg18786873 cg15461516 cg13867865cg09239744 cg09155997 cg09462445 cg14648916 cg13557668 cg09461837cg14936269 cg23697417 cg05171952 cg14409941 cg11428724 cg23932491cg05344430 cg19497031 cg16520288 cg09495977 cg14568217 cg21329599cg27111463 cg05758094 cg21875802 cg18355902 cg06997381 cg08434234cg19178853 cg07017374 cg02842227 cg15424739 cg21176643 cg23215729cg10096161 cg02483484 cg11859584 cg02174225 cg06651311 cg20450979cg06266613 cg15286044 cg17771605 cg09683824 cg16899920 cg07821427cg12859211 cg12686317 cg00625334 cg22284043 cg01878345 cg26990102cg24686074 cg16332256 cg04453180 cg24521633 cg16584573 cg05178576cg22878622 cg16729832 cg27264249 cg19752627 cg24773418 cg01419831cg18646207 cg16514543 cg18762727 cg03257575 cg13776340 cg16474297cg03698948 cg02058731 cg16482474 cg27364741 cg13562911 cg24305584cg15261247 cg26365854 cg11878331 cg04058593 cg18607529 cg06630204cg27101125 cg14725151 cg18759960 cg07057177 cg26615127 cg07068756cg11253514 cg24886267 cg27317433 cg24262066 cg18623980 cg11677857cg02869459 cg13619824 cg22138430 cg14657517 cg01579950 cg06172475cg16307705 cg23201032 cg14535068 cg07752026 cg24403845 cg01501819cg27493301 cg00114029 cg26739280 cg26818735 cg21901946 cg19320476cg26684946 cg23359394 cg27510832 cg00100121 cg26834169 cg00017221cg06319822 cg15409931 cg24876960 cg07078225 cg05562381 cg04156369cg07060006 cg16485558 cg07495363 cg17386213 cg07283152 cg11689407cg13432708 cg24599249 cg25767985 cg21678377 cg13464448 cg18406197cg11107669 cg16366473 cg07628404 cg14256587 cg14667871 cg23719318cg11732619 cg11821817 cg14965220 cg05228284 cg04171539 cg13368519cg07627556 cg20593611 cg17847723 cg11881754 cg06393563 cg19769760cg17483297 cg23978504 cg19924619 cg17263061 cg04100696 cg13652513cg12865552 cg26156687 cg07283114 cg02966153 cg09912350 cg20665002cg08157228 cg26232818 cg21583226 cg15344220 cg08460041 cg21325154cg14218042 cg03142956 cg13670601 cg05332960 cg26892444 cg25184481cg04689080 cg24134479 cg26547924 cg23462956 cg13882278 cg19003797cg11771234 cg04245057 cg13713293 cg02650317 cg01755562 cg10036918cg26029736 cg05710997 cg15867939 cg20168412 cg12424694 cg04549333cg22604123 cg11912330 cg23649435 cg06752260 cg16332936 cg18122419cg19631064 cg27541454 cg08708747 cg09849405 cg00854242 cg10417567cg01138867 cg07600871 cg01278387 cg01056653 cg23820770 cg05141147cg01505767 cg03324821 cg20611276 cg27308329 cg24870497 cg06978388cg19697475 cg23091984 cg12210736 cg16741041 cg26802291 cg09571420cg10173182 cg08386091 cg13353699 cg06499647 cg10188823 cg03336086cg16440629 cg22841810 cg27100436 cg25964032 cg09010323 cg09642925cg08095852 cg01135780 cg24034005 cg06500120 cg08105352 cg20949845cg11953272 cg13025668 cg16882226 cg26686277 cg04130185 cg06738356cg13796804 cg08311610 cg11161828 cg14018731 cg17958315 cg06554120cg07003632 cg08726248 cg27513573 cg17457560 cg26464221 cg13689003cg11733675 cg12850078 cg16589214 cg16434547 cg02979001 cg14602341cg21472506 cg09295081 cg01616178 cg12804010 cg27018185 cg00069860cg12993163 cg20401551 cg22898797 cg05127821 cg07244354 cg13643376cg08042975 cg16236766 cg00592781 cg17507573 cg10031614 cg20232102cg22723056 cg06480695 cg22147084 cg02629281 cg04242021 cg09628601cg17898329 cg14059768 cg16921310 cg13742526 cg08793877 cg05291069cg05624214 cg17838029 cg23048481 cg23226129 cg23582408 cg09424526cg15963563 cg05372242 cg16202470 cg24037897 cg17846334 cg24899571cg11911648 cg25987194 cg18147366 cg25649038 cg25905674 cg16478774cg11775528 cg04548336 cg07491495 cg09441363 cg03132773 cg21233688cg13448814 cg20642710 cg18013519 cg19519310 cg05619587 cg04215672cg11021744 cg26105156 cg26298979 cg02370417 cg23657299 cg24461964cg14088357 cg22403273 cg05812269 cg07559273 cg21098557 cg10903451cg07701191 cg26917673 cg01108106 cg23288962 cg09807215 cg12745764cg07204550 cg20276585 cg08592707 cg23108709 cg12661206 cg15927682cg07455757 cg08943428 cg13911723 cg13482432 cg06371502 cg22685369cg08867893 cg26306372 cg00745606 cg14867604 cg20803857 cg15245095cg11642382 cg15363487 cg13207797 cg13768269 cg26858268 cg22260952cg20072442 cg01154046 cg13443605 cg08172445 cg25616216 cg16303846cg06164660 cg01003015 cg27047243 cg11497952 cg18645133 cg12510981cg27326452 cg27313572 cg07482935 cg26515460 cg07721203 cg08511440cg20650138 cg23991622 cg10254000 cg26077100 cg14042851 cg00024472cg15462174 cg02746869 cg06256858 cg13914094 cg20772101 cg13258563cg20536716 cg18514820 cg15991072 cg22685245 cg02825977 cg09342766cg21864259 cg20319091 cg12630714 cg06959514 cg15212349 cg27557378cg02655972 cg20018469 cg04205107 cg26729197 cg11257429 cg26985666cg18996590 cg14898116 cg08700032 cg12639324 cg04679031 cg16899351cg17293936 cg27147718 cg17498773 cg25130672 cg17999686 cg16482314cg18800085 cg01466678 cg06714320 cg03825010 cg07729537 cg22396057cg02975107 cg11306587 cg15602740 cg07085962 cg04282138 cg26370226cg15803122 cg13031432 cg22587479 cg06530338 cg12874092 cg25165358cg01349858 cg08791131 cg18175808 cg11244340 cg05338433 cg13420848cg26072759 cg02040433 cg15852572 cg23964057 cg14765646 cg14517743cg06270802 cg00984694 cg16086373 cg12097080 cg27230784 cg11163901cg10779644 cg04797985 cg00266322 cg16341592 cg27498114 cg11109374cg05627441 cg00262031 cg26444995 cg20686479 cg16805150 cg04028634cg03840467 cg06650115 cg09324514 cg12584684 cg17108819 cg18488855cg02829680 cg15343461 cg06841192 cg04610224 cg07852757 cg20209009cg02576219 cg11278204 cg07622696 cg08247376 cg04190807 cg01626326cg04819760 cg05245861 cg00758881 cg05006349 cg00095976 cg11189837cg10918202 cg15749748 cg10383447 cg13238990 cg05961935 cg24723331cg12975779 cg07224914 cg08092105 cg02588107 cg03696327 cg25622036cg08145617 cg08828403 cg00581595 cg02361557 cg07952047 cg07034660cg25602490 cg00557354 cg04194840 cg11698244 cg27376683 cg18255353cg06223466 cg04882995 cg12973591 cg01258201 cg08521987 cg22370480cg05203877 cg11137615 cg20230721 cg13631916 cg00584713 cg19025435cg00279790 cg14101302 cg24531255 cg24851364 cg14470398 cg04838988cg21252914 cg14361033 cg17338208 cg20915632 cg11724516 cg10205753cg26215967 cg04279973 cg14775114 cg02756106 cg26256401 cg06078334cg05874561 cg11226148 cg16934178 cg22289831 cg00238770 cg10756127cg12644264 cg05588496 cg22441533 cg16098981 cg15802898 cg21008602cg05525743 cg21187769 cg18302726 cg05238517 cg13769223 cg19265970cg13721429 cg03038003 cg13096260 cg02030008 cg12169536 cg02327123cg06412358 cg22149516 cg08979737 cg24280540 cg05932408 cg23282559cg02795515 cg03091010 cg16935295 cg18565783 cg16120828 cg13761440cg05235761 cg22932815 cg07146119 cg05293738 cg26341102 cg14449051cg04626565 cg08085954 cg20594401 cg02707869 cg07005294 cg00053373cg19918758 cg23089549 cg06531379 cg04336836 cg15308062 cg08372619cg01090834 cg09547173 cg14657834 cg15087147 cg22975913 cg01978558cg13565575 cg08739576 cg24424545 cg13485685 cg12478381 cg27034576cg19123296 cg05082965 cg07748540 cg18233786 cg20513548 cg25664438cg07071978 cg10556384 cg23749856 cg18237607 cg06639332 cg13346411cg09553380 cg22583333 cg25299895 cg01946574 cg05142765 cg05484788cg22871002 cg07135732 cg02500300 cg20250080 cg01562349 cg02855633cg19439399 cg10143067 cg13643796 cg02139871 cg10160975 cg19819145cg15647515 cg09479015 cg10416527 cg03004999 cg21219996 cg19523085cg17883458 cg17475987 cg03352776 cg14374754 cg17350006 cg18736279cg12741994 cg06976395 cg01259029 cg06459000 cg26525127 cg07557790cg03912954 cg01791410 cg07991951 cg06124528 cg02732202 cg24488602cg03802461 cg04207385 cg13436799 cg03774520 cg24141863 cg02753362cg18220921 cg18496247 cg17304222 cg20341985 cg13869401 cg04534926cg08980837 cg25468723 cg18714412 cg24813176 cg26756083 cg01190692cg22130145 cg22376688 cg22675486 cg06498720 cg00057434 cg10694781cg11292593 cg22795590 cg24354581 cg01018701 cg01466288 cg00651020cg27155954 cg18928153 cg13285637 cg04144226 cg13767755 cg25923450cg25214789 cg23589617 cg16793187 cg14242042 cg05310249 cg02489958cg02136132 cg05991314 cg20052751 cg16673106 cg26985446 cg14455998cg10737195 cg24732574 cg06392318 cg13652557 cg01463565 cg19096571cg24862668 cg06025835 cg03350814 cg01775414 cg09198448 cg25302419cg17968795 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cg15577178 cg21437548 cg26983469cg11640773 cg26777303 cg13870510 cg06085011 cg18853199 cg12155165cg27102864 cg23858558 cg27103296 cg11235663 cg25810857 cg13414916cg01084435 cg18719750 cg13875133 cg10737663 cg11468193 cg26063719cg05222604 cg25070637 cg11897314 cg08804846 cg10857221 cg14260889cg00785042 cg14538332 cg14868994 cg27372162 cg25715035 cg19170009cg05129348 cg04261408 cg03247892 cg24482234 cg20631104 cg00687686cg08229468 cg14625631 cg01375976 cg11117108 cg02621694 cg04942472cg09217878 cg10292139 cg02101773 cg23067351 cg01049530 cg05469759cg26514728 cg16673702 cg03167496 cg03954411 cg01941671 cg08384171cg18024479 cg03276479 cg17503456 cg13165472 cg05718036 cg04005075cg19784477 cg23356017 cg09639622 cg17870792 cg26699569 cg16812519cg22799321 cg27517823 cg21527132 cg17965926 cg01285706 cg26824423cg23141855 cg24862252 cg02134353 cg03840647 cg05069909 cg04858398cg23686014 cg10303967 cg07501233 cg14424049 cg07523148 cg05622686cg26739865 cg18030776 cg01578987 cg00723271 cg07321467 cg10146880cg14377593 cg14942501 cg05528102 cg12566138 cg19675063 cg05201312cg15649801 cg26842303 cg26988406 cg10886442 cg10790685 cg02036364cg10539069 cg15980656 cg08937573 cg09470640 cg23821329 cg01733271cg24084681 cg22137815 cg05931096 cg23214267 cg02236650 cg08367638cg07380959 cg14830748 cg02486351 cg14046986 cg19111999 cg02830555cg03333330 cg16962683 cg00187933 cg22082709 cg20198108 cg04808179cg09558850 cg14408978 cg19207487 cg09679945 cg06460869 cg26668272cg19854521 cg00446722 cg16404040 cg11989011 cg05151811 cg05333442cg13328713 cg12190613 cg08614481 cg03167951 cg08918274 cg01343363cg19103770 cg06254440 cg15090727 cg00146951 cg27113419 cg07603382

The selection of a hypermethylated site according to the present methodis defined as follows. For each clinical sample of a specific cancertype in a database, e.g., The Cancer Genome Atlas, the methylation levelis determined for each methylation site i from a starting set of sitesas described in the preceding paragraph. For instance, for each clinicalsample from a set of colon adenocarcinoma samples in The Cancer GenomeAtlas, the methylation state at each of the CpG sites listed in Tables Iand II is determined, and the mean methylation level at each site icalculated as described elsewhere in this application. In someembodiments, the methylation level can be determined as the fraction of‘C’ bases out of ‘C′+′U’ total bases at a target CpG site i followingthe bisulfite treatment. In other embodiments, the methylation level canbe determined as the fraction of ‘C’ bases out of ‘C′+′T’ total bases atsite i following the bisulfite treatment and subsequent nucleic acidamplification. The mean methylation level at each site is then evaluatedto determine if one or more threshold is met. In some embodiments, athreshold selects those sites having the highest-ranked mean methylationvalues for a specific cancer type. For example, the threshold can bethose sites having a mean methylation level that is the top 50%, the top40%, the top 30%, the top 20%, the top 10%, the top 5%, the top 4%, thetop 3%, the top 2%, or the top 1% of mean methylation levels across allsites i tested for a specific cancer type, e.g., colon adenocarcinoma.Alternatively, the threshold can be those sites having a meanmethylation level that is at a percentile rank greater than orequivalent to 50, 60, 70, 80, 90, 95, 96, 97, 98, or 99. In otherembodiments, a threshold can be based on the absolute value of the meanmethylation level. For instance, the threshold can be those sites havinga mean methylation level that is greater than 99%, greater than 98%,greater than 97%, greater than 96%, greater than 95%, greater than 90%,greater than 80%, greater than 70%, greater than 60%, greater than 50%,greater than 40%, greater than 30%, greater than 20%, greater than 10%,greater than 9%, greater than 8%, greater than 7%, greater than 6%,greater than 5%, greater than 4%, greater than 3%, or greater than 2%.The relative and absolute thresholds can be applied to the meanmethylation level at each site i individually or in combination. As anillustration of a combined threshold application, one may select asubset of sites that are in the top 3% of all sites tested by meanmethylation level and also have an absolute mean methylation level ofgreater than 6%. The result of this selection process is a plurality oflists, one for each cancer type, of specific hypermethylated sites(e.g., CpG sites) that are considered the most informative for thatcancer type. These lists are then used to identify or classify a testgenomic DNA sample from a test organism, i.e. to determine whether thetest organism has a specific cancer type.

In the next step of the present method, a test genomic DNA sample from atest organism is analyzed by determining the methylation levels at eachsite i on the list of hypermethylated sites for each cancer type, andthese methylation levels for each site are then averaged to calculatethe average methylation level across the hypermethylated sites for eachcancer type. For instance, for each hypermethylated site i for colonadenocarcinoma, the methylation level at each site i on the list ofhypermethylated sites for colon adenocarcinoma is determined, and thesemethylation levels are then averaged to provide a single averagemethylation level. This process is repeated using the previously definedlists of hypermethylated sites for each of the cancer types, and resultsin a set of average methylation levels, each corresponding to adifferent cancer type. The average methylation levels are then rankedfrom lowest to highest. The cancer type corresponding to the highestaverage methylation level is considered to be associated with the testgenomic DNA, i.e. the cancer type is deemed to be present in the testorganism. It is understood that the normalized methylation difference orz-score also can be used in the present method instead of themethylation level at each CpG site.

For cancer screening or detection, the determination of a methylationlevel of a plasma (or other biologic) sample can be used in conjunctionwith other modalities for cancer screening or detection such as prostatespecific antigen measurement (e.g. for prostate cancer),carcinoembryonic antigen (e.g. for colorectal carcinoma, gastriccarcinoma, pancreatic carcinoma, lung carcinoma, breast carcinoma,medullary thyroid carcinoma), alpha fetoprotein (e.g. for liver canceror germ cell tumors) and CA19-9 (e.g. for pancreatic carcinoma).

Useful methylation sites that can be detected in a method set forthherein, for example, to evaluate cancer are include those present in theCancer Genome Atlas (see, for example, Cancer Genome Atlas ResearchNetwork et al., Nature Genetics 45:1113-1120 (2013)) or the selected CpGsites of the Pan Cancer Panel set forth in Table I (the listedmethylation sites are from Genome Build 37). Further examples of CpGsites that can be useful, for example, to identify or monitor colorectalcancer, are described in Worthley et al. Oncogene 29, 1653-1662 (2010)or set forth in Table II (the listed methylation sites are from GenomeBuild 37). Useful methylation markers for detection of ovarian cancerare set forth in US Pat. App. Pub. No. 2008/0166728 A1, which isincorporated herein by reference. All or a subset of the markers setforth herein and/or listed in a reference above can be used in a methodset forth herein. For example, at least 10, 25, 50, 100, 1×10³, 1×10⁴ ormore of the markers can be used.

Analysis of the methylation, prognosis or diagnosis information derivedfrom a method set forth herein can conveniently be performed usingvarious computer executed algorithms and programs. Therefore, certainembodiments employ processes involving data stored in or transferredthrough one or more computer systems or other processing systems.Embodiments of the invention also relate to apparatus for performingthese operations. This apparatus may be specially constructed for therequired purposes, or it may be a general-purpose computer (or a groupof computers) selectively activated or reconfigured by a computerprogram and/or data structure stored in the computer. In someembodiments, a group of processors performs some or all of the recitedanalytical operations collaboratively (e.g., via a network or cloudcomputing) and/or in parallel. A processor or group of processors forperforming the methods described herein may be of various typesincluding microcontrollers and microprocessors such as programmabledevices (e.g., CPLDs and FPGAs) and non-programmable devices such asgate array ASICs or general purpose microprocessors.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude program instructions and/or data (including data structures) forperforming various computer-implemented operations. Examples ofcomputer-readable media include, but are not limited to, semiconductormemory devices, magnetic media such as disk drives, magnetic tape,optical media such as CDs, magneto-optical media, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and random access memory (RAM).The computer readable media may be directly controlled by an end user orthe media may be indirectly controlled by the end user. Examples ofdirectly controlled media include the media located at a user facilityand/or media that are not shared with other entities. Examples ofindirectly controlled media include media that is indirectly accessibleto the user via an external network and/or via a service providingshared resources such as a “cloud.” A particularly useful cloud is onethat is configured and administered to store and analyze genetic datasuch as the BaseSpace™ service (Illumina, Inc. San Diego Calif.), orcloud services described in US Pat. App. Pub. Nos. 2013/0275486 A1 or2014/0214579 A1 (each of which is incorporated herein by reference).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

In some embodiments, the data or information employed in the disclosedmethods and apparatus is provided in an electronic format. Such data orinformation may include reads derived from a nucleic acid sample,reference sequences, methylation states, patterns of methylation states,methylation difference scores, normalized methylation difference scores,aggregate coverage-weighted normalized methylation difference scores,methylation scores, coverage-weighted methylation scores, counselingrecommendations, diagnoses, and the like. As used herein, data or otherinformation provided in electronic format is available for storage on amachine and transmission between machines. Conventionally, data inelectronic format is provided digitally and may be stored as bits and/orbytes in various data structures, lists, databases, etc. The data may beembodied electronically, optically, etc.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude instructions and/or data (including data structures) forperforming various computer-implemented operations. One or more of thesteps of a method set forth herein can be carried out by a computerprogram that is present in tangible and/or non-transitory computerreadable media, or carried out using computer hardware.

For example, a computer program product is provided and it comprises anon-transitory computer readable medium on which is provided programinstructions for steps of (a) obtaining a test data set that includes(i) methylation states for a plurality of sites from test genomic DNAfrom at least one test organism, and (ii) coverage at each of the sitesfor detection of the methylation states; (b) obtaining methylationstates for the plurality of sites in reference genomic DNA from one ormore reference individual organisms, (c) determining, for each of thesites, the methylation difference between the test genomic DNA and thereference genomic DNA, thereby providing a normalized methylationdifference for each site; (d) weighting the normalized methylationdifference for each site by the coverage at each of the sites, therebydetermining an aggregate coverage-weighted normalized methylationdifference score, and (e) storing or transmitting the aggregatecoverage-weighted normalized methylation difference score.

Methods disclosed herein can also be performed using a computerprocessing system which is adapted or configured to perform a method foridentifying methylation states or other characteristics of nucleicacids. Thus, in one embodiment, the invention provides a computerprocessing system which is adapted or configured to perform a method asdescribed herein. In one embodiment, the apparatus comprises a nucleicacid detection device, such as a nucleic acid sequencing device, adaptedor configured to determine methylation states and/or othercharacteristics of nucleic acids. The apparatus may also includecomponents for processing a sample from a test organism and/or referenceorganism. Such components are described elsewhere herein.

Nucleic acid sequence, methylation state, methylation pattern, or otherdata, can be input into a computer or stored on a computer readablemedium either directly or indirectly. In one embodiment, a computersystem is directly coupled to a nucleic acid detection device (e.g.sequencing device) that determines methylation states of nucleic acidsfrom samples. Data or other information from such tools are provided viainterface in the computer system. Alternatively, the methylation dataprocessed by systems are provided from a data storage source such as adatabase or other repository. Once available to the processingapparatus, a memory device or mass storage device buffers or stores, atleast temporarily, methylation states or other characteristics of thenucleic acids. In addition, the memory device may store methylationdifferences, normalized methylation differences, aggregate weightednormalized methylation differences, methylation scores, orcoverage-weighted methylation scores as described herein. The memory mayalso store various routines and/or programs for analyzing or presentingsuch information. Such programs/routines may include programs forperforming statistical analyses, etc.

In one example, a user provides a sample to a nucleic acid sequencingapparatus. Data is collected and/or analyzed by the sequencing apparatuswhich is connected to a computer. Software on the computer allows fordata collection and/or analysis. Data can be stored, displayed (e.g. viaa monitor or other similar device), and/or sent to another location. Thecomputer may be connected to the internet which is used to transmit datato a handheld device and/or cloud environment utilized by a remote user(e.g., a physician, scientist or analyst). It is understood that thedata can be stored and/or analyzed prior to transmittal. In someembodiments, raw data is collected and sent to a remote user orapparatus that will analyze and/or store the data. Transmittal can occurvia the internet, but can also occur via satellite or other connection.Alternately, data can be stored on a computer-readable medium and themedium can be shipped to an end user (e.g., via mail). The remote usercan be in the same or a different geographical location including, butnot limited to, a building, city, state, country or continent.

In some embodiments, the methods also include collecting data regardinga plurality of polynucleotide sequences (e.g., reads, tags and/ormethylation states) and sending the data to a computer or othercomputational system. For example, the computer can be connected tolaboratory equipment, e.g., a sample collection apparatus, a nucleotideamplification apparatus, a nucleotide sequencing apparatus, or ahybridization apparatus. The computer can then collect applicable datagathered by the laboratory device. The data can be stored on a computerat any step, e.g., while collected in real time, prior to the sending,during or in conjunction with the sending, or following the sending. Thedata can be stored on a computer-readable medium that can be extractedfrom the computer. The data that has been collected or stored can betransmitted from the computer to a remote location, e.g., via a localnetwork or a wide area network such as the internet. At the remotelocation various operations can be performed on the transmitted data asdescribed below.

Among the types of electronically formatted data that may be stored,transmitted, analyzed, and/or manipulated in systems, apparatus, andmethods disclosed herein are the following: reads obtained by sequencingnucleic acids in a test sample, methylation states for sites in thenucleic acids, one or more reference genome or sequence, methylationdifference score, normalized methylation difference score, aggregatecoverage-weighted normalized methylation difference score, methylationscore, or coverage-weighted methylation score as described herein.

These various types of data may be obtained, stored, transmitted,analyzed, and/or manipulated at one or more locations using distinctapparatus. The processing options span a wide spectrum. Toward one endof the spectrum, all or much of this information is stored and used atthe location where the test sample is processed, e.g., a doctor's officeor other clinical setting. Toward another extreme, the sample isobtained at one location, it is processed (e.g. prepared, detected orsequenced) at a second location, data is analyzed (e.g. sequencing readsare aligned) and methylation characteristics are determined at a thirdlocation (or several locations), and diagnoses, recommendations, and/orplans are prepared at a fourth location (or the location where thesample was obtained).

In various embodiments, the methylation data are generated on a nucleicacid detection apparatus (e.g. sequencing apparatus) and thentransmitted to a remote site where they are processed to determinemethylation characteristics. At this remote location, as an example,methylation difference score, normalized methylation difference score,aggregate coverage-weighted normalized methylation difference score,methylation score, or coverage-weighted methylation score can bedetermined. Also at the remote location, the methylation characteristicscan be evaluated to make a prognostic or diagnostic determination.

Any one or more of these operations may be automated as describedelsewhere herein. Typically, the detection of nucleic acids and theanalyzing of sequence data will be performed computationally. The otheroperations may be performed manually or automatically.

Examples of locations where sample collection may be performed includehealth practitioners' offices, clinics, patients' homes (where a samplecollection tool or kit is provided), and mobile health care vehicles.Examples of locations where sample processing prior to methylationdetection may be performed include health practitioners' offices,clinics, patients' homes (where a sample processing apparatus or kit isprovided), mobile health care vehicles, and facilities of nucleic acidanalysis providers. Examples of locations where nucleic acid detection(e.g. sequencing) may be performed include health practitioners'offices, clinics, health practitioners' offices, clinics, patients'homes (where a sample sequencing apparatus and/or kit is provided),mobile health care vehicles, and facilities of nucleic acid analysisproviders. The location where the nucleic acid detection takes place maybe provided with a dedicated network connection for transmittingsequence data (typically reads) in an electronic format. Such connectionmay be wired or wireless and may be configured to send the data to asite where the data can be processed and/or aggregated prior totransmission to a processing site. Data aggregators can be maintained byhealth organizations such as Health Maintenance Organizations (HMOs).

The analyzing operations may be performed at any of the foregoinglocations or alternatively at a further remote site dedicated tocomputation and/or the service of analyzing nucleic acid sequence data.Such locations include for example, clusters such as general purposeserver farms, the facilities of a genetic analysis service business, andthe like. In some embodiments, the computational apparatus employed toperform the analysis is leased or rented. The computational resourcesmay be part of an internet accessible collection of processors such asprocessing resources colloquially known as the “cloud”, examples ofwhich are provided elsewhere herein. In some cases, the computations areperformed by a parallel or massively parallel group of processors thatare affiliated or unaffiliated with one another. The processing may beaccomplished using distributed processing such as cluster computing,grid computing, and the like. In such embodiments, a cluster or grid ofcomputational resources collective form a super virtual computercomposed of multiple processors or computers acting together to performthe analysis and/or derivation described herein. These technologies aswell as more conventional supercomputers may be employed to processsequence data as described herein. Each is a form of parallel computingthat relies on processors or computers. In the case of grid computingthese processors (often whole computers) are connected by a network(private, public, or the Internet) by a conventional network protocolsuch as Ethernet. By contrast, a supercomputer has many processorsconnected by a local high-speed computer bus.

In certain embodiments, the diagnosis (e.g., determination that thepatient has a particular type of cancer) is generated at the samelocation as the analyzing operation. In other embodiments, it isperformed at a different location. In some examples, reporting thediagnosis is performed at the location where the sample was taken,although this need not be the case. Examples of locations where thediagnosis can be generated or reported and/or where developing a plan isperformed include health practitioners' offices, clinics, internet sitesaccessible by computers, and handheld devices such as cell phones,tablets, smart phones, etc. having a wired or wireless connection to anetwork. Examples of locations where counseling is performed includehealth practitioners' offices, clinics, Internet sites accessible bycomputers, handheld devices, etc.

In some embodiments, the sample collection, sample processing, andmethylation state detection operations are performed at a first locationand the analyzing and deriving operation is performed at a secondlocation. However, in some cases, the sample collection is collected atone location (e.g., a health practitioner's office or clinic) and thesample processing and methylation state detecting is performed at adifferent location that is optionally the same location where theanalyzing and deriving take place.

In various embodiments, a sequence of the above-listed operations may betriggered by a user or entity initiating sample collection, sampleprocessing and/or methylation state detection. After one or more ofthese operations have begun execution the other operations may naturallyfollow. For example, a nucleic acid sequencing operation may cause readsto be automatically collected and sent to a processing apparatus whichthen conducts, often automatically and possibly without further userintervention, the methylation state analysis and determination ofmethylation difference score, normalized methylation difference score,aggregate coverage-weighted normalized methylation difference score,methylation score, or coverage-weighted methylation score. In someimplementations, the result of this processing operation is thenautomatically delivered, possibly with reformatting as a diagnosis, to asystem component or entity that processes or reports the information toa health professional and/or patient. As explained, such information canalso be automatically processed to produce a treatment, testing, and/ormonitoring plan, possibly along with counseling information. Thus,initiating an early stage operation can trigger an end to end process inwhich the health professional, patient or other concerned party isprovided with a diagnosis, a plan, counseling and/or other informationuseful for acting on a physical condition. This is accomplished eventhough parts of the overall system are physically separated and possiblyremote from the location of, e.g., the sample collection and nucleicacid detection apparatus.

In some embodiments the results of a method set forth herein will becommunicated to an individual by a genetic counselor, physician (e.g.,primary physician, obstetrician, etc.), or other qualified medicalprofessional. In certain embodiments the counseling is providedface-to-face, however, it is recognized that in certain instances, thecounseling can be provided through remote access (e.g., via text, cellphone, cell phone app, tablet app, internet, and the like).

In some embodiments, disclosure of results to a medical professional orto a patient can be delivered by a computer system. For example, “smartadvice” systems can be provided that in response to test results,instructions from a medical care provider, and/or in response to queries(e.g., from a patient) provide genetic counseling information. Incertain embodiments the information will be specific to clinicalinformation provided by the physician, healthcare system, and/orpatient. In certain embodiments the information can be provided in aniterative manner. Thus, for example, the patient can provide “what if”inquiries and the system can return information such as diagnosticoptions, risk factors, timing, and implication of various outcomes.

In particular embodiments, the results or other information generated ina method set forth herein can be provided in a transitory manner (e.g.,presented on a computer screen). In certain embodiments, the informationcan be provided in a non-transitory manner. Thus, for example, theinformation can be printed out (e.g., as a list of options and/orrecommendations optionally with associated timing, etc.) and/or storedon computer readable media (e.g., magnetic media such as a local harddrive, a server, etc., optical media, flash memory, and the like).

It will be appreciated that typically such systems will be configured toprovide adequate security such that patient privacy is maintained, e.g.,according to prevailing standards in the medical field.

The foregoing discussion of genetic counseling is intended to beillustrative and not limiting. Genetic counseling is a well-establishedbranch of medical science and incorporation of a counseling componentwith respect to the methods described herein is within the scope andskill of the practitioner. Moreover, it is recognized that as the fieldprogresses, the nature of genetic counseling and associated informationand recommendations is likely to alter.

Example I Analytical Sensitivity of ctDNA Methylation-Based CancerDetection Using Aggregate Normalized Coverage-Weighted MethylationDifferences

This example describes a highly sensitive assay for detectingmethylation in circulating tumor DNA (ctDNA). Aberrant DNA methylationis a widespread phenomenon in cancer and may be among the earliestchanges to occur during oncogenesis. The assay described in this examplecan be useful for cancer screening.

The general approach applied here includes targeted methylationsequencing for multiple CpG sites affected in cancer.

Technical challenges addressed by the approach include providingultra-high sensitivity and specificity that benefits screeningapplications, providing a protocol for targeted methyl-seq from lowinput ctDNA, and providing bioinformatics algorithms for analysis ofmethylation levels across a large number of targeted sites.

Targeted Capture Probe Design

Two targeted methylation panels were developed. The Pan-Cancer Paneltargets 9,921 affected CpG sites in 20 major cancer types as selectedfrom The Cancer Genome Atlas Database. The CpG sites included in thePan-Cancer Panel are listed in Table I. The CRC Panel targets 1,162affected CpG sites in colorectal cancer. The CpG sites included in theCRC Panel are listed in Table II. The CpG sites listed in Table I andTable II refer to Genome Build 37.

The probe sequences for the CpG sites were selected from the InfiniumHM450 array (Illumina, Inc., San Diego, Calif.). Design principles forthe probes are shown in FIG. 1. Two probes were used for targets havinggreater than 4 CpG sites, including a completely methylated probe(having a G nucleotides that complements the C position of each CpGsite) and completely unmethylated probe (having an A nucleotide thatcomplements the U that is expected to result from bisulfite conversionof each of the C positions of a CpG site) as shown in FIG. 1. Incontrast, only one probe was used for targets having 4 or fewer CpGsites (the probe includes degenerate nucleotide R, complementary to U orC, at the C position of each CpG site).

Isolation and Extraction of cfDNA from Plasma

Plasma samples were obtained from human blood draws. Cell free DNA(cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit(Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing wascarried out according to the workflow shown in FIG. 2, and as set forthbelow in the context of evaluating titration and detection sensitivity.

Titration and Detection Sensitivity

NA12878 genomic DNA was purchased from Coriell Institute (CoriellInstitute, Camden, N.J.), and LS1034 genomic DNA was purchased from ATCC(ATCC, Manassas, Va.). Genomic DNA was fragmented using Covaris M200(Covaris, Woburn, Mass.) and size-selected to 130-250 bp usingBluePippin (Sage Science, Beverly, Mass.) to simulate the sizedistribution of cfDNA. DNA quantification was performance usingQuant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, GrandIsland, N.Y.). 10%, 1%, or 0.1% LS1034 DNA was spiked into NA12878 DNAbackground to make the DNA mixtures. 30 ng of each mixture, 100%NA12878, or 100% LS1034 DNA was used in library preparation. Threereplicated libraries were generated for each titration level. A set ofsix replicates of NA12878 was used as the baseline reference genome.

Extracted cfDNA or sheared and size-selected genomic DNA was bisulfitetreated and purified using EZ DNA Methylation-Lightning Kit (ZymoResearch, Irvine, Calif.).

Bisulfite-seq Libraries were prepared using the Accel-NGS® Methyl-SeqDNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries usingprobes that were complementary to fragments having the CpG sites listedin Table I or Table II. Capture probes were synthesized and biotinylatedat Illumina, Inc. Target capture was performed using Illumina TruSight™Rapid Capture Kit according to manufacturer's instructions except thatcustomized capture probes were used, and hybridization and wash stepswere performed at 48 C.

The products of the capture step were sequenced on an Illumina HiSeq2500 Sequencer using 2×100 cycle runs, with four samples in rapid runmode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylationwhereby all C's on read 1 were converted to T's and all G's on read 2were converted to A's. Subsequently, these “demethylated” FASTQsequences were aligned using BWA (v 0.7.10-r789) to an index comprisinga “demethylated” hg19 genome. BWA alignment is described in Li andDurbin (2010) Fast and accurate long-read alignment with Burrows-WheelerTransform. Bioinformatics, Epub. [PMID: 20080505], which is incorporatedherein by reference. Following alignment, the “demethylated” FASTQsequences were replaced with the original FASTQ sequences. Methylationlevels were calculated as the fraction of ‘C’ bases at a target CpG siteout of ‘C’+‘T’ total bases.

Following calculation of methylation levels at each CpG site for eachsample and replicate, aggregate coverage-weighted normalized methylationdifference z-scores were calculated as follows.

(1) the methylation level at each CpG site was normalized by subtractingthe mean methylation level in baseline and dividing by the standarddeviation of methylation levels in baseline to obtain a per-sitez-score. Specifically, the normalized methylation difference at each CpGsite was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$where Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA.

(2) the z-score at each CpG site was multiplied by the coverage observedat the CpG site, and the coverage-weighted z-score was then summedacross all CpG sites and then divided by the sum of the coverage squaredat each CpG site. More specifically, an aggregate coverage-weightedmethylation difference z-score (an example of an aggregatecoverage-weighted normalized methylation difference score, A) wasdetermined according to the formula:

$A = \frac{\sum_{i = 1}^{k}\;{w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}\; w_{i}^{2}}}$where w_(i) represents the coverage at site i and k represents the totalnumber of sites.Results

A titration experiment was performed to demonstrate analyticalsensitivity using a colorectal cancer cell line LS1034 and a normal cellline NA12878. Namely, targeted ctDNA methylation sequencing wasperformed in triplicates using both the Pan-Cancer and CRC panels on0.1%, 1%, and 10% titrations of LS1034 into NA12878 along with pureLS1034 and pure NA12878. For each of the 15 sample replicates, theaggregate coverage-weighted methylation difference z-scores werecalculated using the normal NA12878 samples as the baseline (FIG. 3).The results indicate that the within sample variation is far less thanthe variation between titration levels. In particular, the clearseparation of the 0.1% titration of LS1034 into NA12878 from the NA12878sample indicates the assay and accompanying aggregate coverage-weightedmethylation difference z-score can achieve a 0.1% limit of detection.

Results obtained using the methods of this example provide highsensitivity evaluation of the cumulative effect of multiple affected CpGsites across the genome. By providing a method for detecting methylationpatterns the methods of this example can provide improved cancerdiagnosis than methods that rely on detection of somatic mutations, asevidenced by the improved concordance in alternations between CRC tissueand corresponding plasma when evaluating DNA methylation markerscompared to somatic mutations (see, for example, Danese et al.,“Comparison of Genetic and Epigenetic Alterations of Primary Tumors andMatched Plasma Samples in Patients with Colorectal Cancer” PLoS ONE10(5):e0126417. doi:10.1371/journal.pone.0126417 (2015), which isincorporated herein by reference). The methods described in this examplealso provide identification of tissue origin for cancer. Specifically,tissue specific methylation markers have been shown to be useful totrace the tissue origin of particular ctDNA sequences (see, for example,Sun et al. “Plasma DNA tissue mapping by genome-wide methylationsequencing for noninvasive prenatal, cancer, and transplantationassessments” Proc. Natl. Acad. Sci, USA 112 (40) E5503-E5512 (2015),which is incorporated herein by reference).

Example II Analytical Sensitivity of ctDNA Methylation-Based CancerDetection Using Coverage-Weighted Methylation Scores

This example describes an alternative highly sensitive assay fordetecting methylation in circulating tumor DNA (ctDNA). The assaydescribed in this example also can be useful for cancer screening,monitoring disease progression, or evaluating a patient's response to atherapeutic treatment.

Targeted Capture Probe Design

For this study, the two targeted methylation panels described in ExampleI were pooled together. The Pan-Cancer Panel targets 9,921 affected CpGsites in 20 major cancer types as selected from The Cancer Genome AtlasDatabase. The CpG sites included in the Pan-Cancer Panel are listed inTable I. The CRC Panel targets 1,162 affected CpG sites in colorectalcancer. The CpG sites included in the CRC Panel are listed in Table II.The combined CpG sites listed in Table I and Table II refer to GenomeBuild 37.

The probe sequences for the CpG sites were selected from the InfiniumHM450 array (Illumina, Inc., San Diego, Calif.). Design principles forthe probes are shown in FIG. 1. Two probes were used for targets havinggreater than 4 CpG sites, including a completely methylated probe(having a G nucleotides that complements the C position of each CpGsite) and completely unmethylated probe (having an A nucleotide thatcomplements the U that is expected to result from bisulfite conversionof each of the C positions of a CpG site) as shown in FIG. 1. Incontrast, only one probe was used for targets having 4 or fewer CpGsites (the probe includes degenerate nucleotide R, complementary to U orC, at the C position of each CpG site).

Isolation and Extraction of cfDNA from Plasma

Plasma samples were obtained from human blood draws. Cell free DNA(cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit(Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing wascarried out according to the workflow shown in FIG. 2, and as set forthbelow in the context of evaluating titration and detection sensitivity.

Titration and Detection Sensitivity

As described above, NA12878 genomic DNA was purchased from CoriellInstitute (Coriell Institute, Camden, N.J.), and LS1034 genomic DNA waspurchased from ATCC (ATCC, Manassas, Va.). Genomic DNA was fragmentedusing Covaris M200 (Covaris, Woburn, Mass.) and size-selected to 130-250bp using BluePippin (Sage Science, Beverly, Mass.) to simulate the sizedistribution of cfDNA. DNA quantification was performance usingQuant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, GrandIsland, N.Y.). 10%, 1%, or 0.1% LS1034 DNA was spiked into NA12878 DNAbackground to make the DNA mixtures. 30 ng of each mixture, 100%NA12878, or 100% LS1034 DNA was used in library preparation. Threereplicated libraries were generated for each titration level. A set ofsix replicates of NA12878 was used as the baseline reference genome.

Extracted cfDNA or sheared and size-selected genomic DNA was bisulfitetreated and purified using EZ DNA Methylation-Lightning Kit (ZymoResearch, Irvine, Calif.).

Bisulfite-seq Libraries were prepared using the Accel-NGS® Methyl-SeqDNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries usingprobes that were complementary to fragments having the CpG sites listedin Table I or Table II. Capture probes were synthesized and biotinylatedat Illumina, Inc. Target capture was performed using Illumina TruSight™Rapid Capture Kit according to manufacturer's instructions except thatcustomized capture probes were used, and hybridization and wash stepswere performed at 48 C.

The products of the capture step were sequenced on an Illumina HiSeq2500 Sequencer using 2×100 cycle runs, with four samples in rapid runmode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylationwhereby all C's on read 1 were converted to T's and all G's on read 2were converted to A's. Subsequently, these “demethylated” FASTQsequences were aligned using BWA (v 0.7.10-r789) to an index comprisinga “demethylated” hg19 genome. BWA alignment is described in Li andDurbin (2010) Fast and accurate long-read alignment with Burrows-WheelerTransform. Bioinformatics, Epub. [PMID: 20080505], which is incorporatedherein by reference. Following alignment, the “demethylated” FASTQsequences were replaced with the original FASTQ sequences. Methylationlevels were calculated as the fraction of ‘C’ bases at a target CpG siteout of ‘C’+‘T’ total bases.

After calculation of methylation levels at each CpG site for each sampleand replicate, coverage-weighted methylation scores were calculated asfollows.

(1) The methylation level at each CpG site was normalized by subtractingthe mean methylation level in baseline and dividing by the standarddeviation of methylation levels in the baseline to obtain a per-sitez-score. Specifically, the normalized methylation difference at each CpGsite was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$where Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA.

(2) The z-score for each CpG site i (Z_(i)) was converted into theprobability of observing such a z-score or greater by converting thez-score into a one-sided p-value (p_(i)). Probabilities were calculatedassuming a normal distribution, although other distributions (e.g.,t-distribution or binomial distribution) may be used as well.

(3) The p-value at each CpG site was weighted by multiplying the p-valueat each CpG site i (p_(i)) by the coverage observed at the CpG site(w_(i)), and a coverage-weighted methylation score (MS) was determinedby combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( {w_{i}p_{i}} \right)}}}$where p_(i) represents the one-sided p-value at site i, k represents thetotal number of sites, and w_(i) represents the coverage at site i.Results

A titration experiment was performed to demonstrate analyticalsensitivity using a colorectal cancer cell line LS1034 and a normal cellline NA12878. Namely, targeted ctDNA methylation sequencing wasperformed in triplicates using the combined Pan-Cancer and CRC panels on0.1%, 1%, and 10% titrations of LS1034 into NA12878 along with pureLS1034 and pure NA12878. For each of the 15 sample replicates, thecoverage-weighted methylation scores were calculated using the normalNA12878 samples as the baseline (FIG. 4). The results indicate that thewithin sample variation is far less than the variation between titrationlevels. In particular, the clear separation of the 0.1% titration ofLS1034 into NA12878 from the NA12878 sample indicates the assay andaccompanying coverage-weighted methylation score can achieve a 0.1%limit of detection (see inset of FIG. 4).

Similar to the results in Example I, results obtained using the methodsof this example provide high sensitivity evaluation of the cumulativeeffect of multiple affected CpG sites across the genome. By providing analternative method for detecting methylation patterns, the methods ofthis example can provide a more sensitive cancer diagnosis than methodsrelying on detection of somatic mutations.

Example III Clinical Performance of ctDNA Methylation-Based CancerDetection Using Normalized Coverage-Weighted Methylation ScoreDifferences

This example evaluates clinical sensitivity and specificity of themethylation-based cancer detection in circulating tumor DNA (ctDNA)using normalized coverage weighted methylation score differences. Asnoted above, the assay described in this example can be useful forcancer screening, monitoring disease progression, or evaluating apatient's response to a therapeutic treatment.

Targeted Capture Probe Design

For this study, the two targeted methylation panels described in ExampleI were pooled together. The Pan-Cancer Panel targets 9,921 affected CpGsites in 20 major cancer types as selected from The Cancer Genome AtlasDatabase. The CpG sites included in the Pan-Cancer Panel are listed inTable I. The CRC Panel targets 1,162 affected CpG sites in colorectalcancer. The CpG sites included in the CRC Panel are listed in Table II.The combined CpG sites listed in Table I and Table II refer to GenomeBuild 37.

The probe sequences for the CpG sites were selected from the InfiniumHM450 array (Illumina, Inc., San Diego, Calif.). Design principles forthe probes are shown in FIG. 1. Two probes were used for targets havinggreater than 4 CpG sites, including a completely methylated probe(having a G nucleotides that complements the C position of each CpGsite) and completely unmethylated probe (having an A nucleotide thatcomplements the U that is expected to result from bisulfite conversionof each of the C positions of a CpG site) as shown in FIG. 1. Incontrast, only one probe was used for targets having 4 or fewer CpGsites (the probe includes degenerate nucleotide R, complementary to U orC, at the C position of each CpG site).

Blood Sample Collection and Processing

Cancer patients were recruited at MD Anderson Cancer Center (Houston,Tex.). A total of 70 blood samples collected from 63 late stage cancerpatients of three cancer types were used in this study (n=30 forcolorectal cancer (CRC), n=14 for breast cancer (BRCA), n=19 for lungcancer). Four CRC patients had blood samples collected at multiple timepoints. Three breast cancer samples and one colorectal cancer samplefailed sample quality control and therefore were excluded from theanalysis, resulting in the final set of 66 cancer samples (36 CRC, 11BRCA, and 19 lung), representing 59 different patients (29 CRC, 11 BRCA,and 19 lung). A total of 65 normal blood samples were collected fromhealthy subjects to be used as baseline methylation controls (20),training controls (20) and testing controls (25) as described herein.

Plasma was separated by centrifugation at 1600 G for 10 minutes. Thesupernatant was transferred to 15 mL centrifuge tubes and centrifuged atroom temperature for 10 minutes at 3000 G. The supernatant wastransferred to a fresh 15 mL centrifuge tube and stored in a freezer(−80° C.) and shipped on dry ice. Plasma samples from healthy donorswere obtained from BioreclamationIVT (Westbury, N.Y.). All samples werede-identified.

Isolation and Extraction of cfDNA from Plasma

Cell free DNA (cfDNA) was extracted using the QIAamp Circulating NucleicAcid Kit (Qiagen, Hilden, Germany). Targeted ctDNA methylationsequencing was carried out according to the workflow shown in FIG. 2,and as set forth below in the context of evaluating titration anddetection sensitivity.

Targeted Bisulfite Sequencing Library Preparation and Sequencing

cfDNA was bisulfite treated and purified using EZ DNAMethylation-Lightning Kit (Zymo Research, Irvine, Calif.).

Whole genome amplification of bisulfite-converted DNA was performedusing Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, AnnArbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries usingprobes that were complementary to fragments having the CpG sites listedin Tables I and II. Capture probes were synthesized and biotinylated atIllumina, Inc. (San Diego, Calif.). Target capture was performed usingIllumina TruSight™ Rapid Capture Kit according to manufacturer'sinstructions. Hybridization and wash conditions were modified to yieldoptimal capture efficiency.

The products of the capture step were sequenced on an Illumina Hiseq2500Sequencer using 2×100 cycle runs, with four samples in rapid run mode,according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylationwhereby all C's on read 1 were converted to T's and all G's on read 2were converted to A's. Subsequently, these “demethylated” FASTQsequences were aligned using BWA (v 0.7.10-r789) to an index comprisinga “demethylated” hg19 genome. BWA alignment is described in Li andDurbin (2010) Fast and accurate long-read alignment with Burrows-WheelerTransform. Bioinformatics, Epub. [PMID: 20080505], which is incorporatedherein by reference. Following alignment, the “demethylated” FASTQsequences were replaced with the original FASTQ sequences. Methylationlevels were calculated as the fraction of ‘C’ bases at a target CpG siteout of ‘C’+‘T’ total bases.

After calculation of methylation levels at each CpG site for each sampleand replicate, coverage-weighted methylation scores were calculated asfollows.

(1) Methylation scores were initially determined for the training set of20 normal genomic DNA samples. First, a normalized methylationdifference (z-score) at a particular site i (e.g., CpG site) wasdetermined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in a member of the training set of normal genomic DNA, μ_(i)represents the mean methylation level at site i in the baseline samples,and σ_(i) represents the standard deviation of methylation levels atsite i in the baseline samples.

(2) The z-score for each CpG site i (Z_(i)) was converted into theprobability of observing such a z-score or greater by converting thez-score into a one-sided p-value (p_(i)). Probabilities were calculatedassuming a normal distribution, although other distributions (e.g.,t-distribution or binomial distribution) may be used as well.

(3) The p-value at each CpG site was weighted by multiplying the p-valueat each CpG site i (p_(i)) by the coverage observed at the CpG site(w_(i)), and a coverage-weighted methylation score (MS) was determinedby combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( {w_{i}p_{i}} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of sites, and w_(i) represents the significance, forinstance coverage, of the site i.

(4) Statistical analysis of the training set methylation scores was thenperformed. The mean methylation score (μ_(MS)) and standard deviation ofmethylation scores (σ_(MS)) in the training set of normal genomic DNAwere calculated, characterizing the distribution of the methylationscore in a normal population.

(5) Next, methylation scores were determined for the 66 cancer genomicDNA samples and 25 testing controls. First, a normalized methylationdifference (z-score) at each CpG site was determined according to theformula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$where Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA.

(6) The z-score for each CpG site i (Z_(i)) was converted into theprobability of observing such a z-score or greater by converting thez-score into a one-sided p-value (p_(i)). Probabilities were calculatedassuming a normal distribution, although other distributions (e.g.,t-distribution or binomial distribution) may be used as well.

(7) The p-value at each CpG site was weighted by multiplying the p-valueat each CpG site i (p_(i)) by the coverage observed at the CpG site(w_(i)), and a coverage-weighted methylation score (MS) was determinedby combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\;{\ln\left( {w_{i}p_{i}} \right)}}}$where p_(i) represents the one-sided p-value at site i, k represents thetotal number of sites, and w_(i) represents the coverage at site i.

(8) Finally, the methylation scores of the test genomic DNA samples wereevaluated against the distribution of methylation scores determined forthe training set population, represented by the mean methylation score(μ_(MS)) and standard deviation of methylation scores (σ_(MS)) for thetraining set of normal genomic DNA. The number of standard deviationsbetween the methylation score for the test genomic DNA and themethylation score mean (μ_(MS)) of the training set of normal genomicDNA was determined according to the formula:

$Z_{MS} = \frac{{MS} - µ_{MS}}{\sigma_{MS}}$wherein Z_(MS) represents a normalized methylation score difference, MSrepresents the methylation score of the test sample, μ_(MS) representsthe mean methylation score for the training set of normal genomic DNA,and σ_(MS) represents the standard deviation of methylation scores forthe training set of normal genomic DNA. A Z_(MS) value greater than 3standard deviations was used as a threshold to identify cancer samples.Results

As noted above, the purpose of this experiment was to evaluate theclinical performance of the normalized coverage-weighted methylationscore difference algorithm, including its clinical sensitivity andspecificity. The 66 cancer samples and 25 normal samples were subjectedto the methylation score analysis as described herein, includingdetermining the z-score for each of the CpG sites listed in Tables I andII, converting the z-score into a one-sided p-value based on a normaldistribution assumption, weighting the p-values by coverage, andaggregating the individual weighted p-values into a single methylationscore using the Fisher formula. The resulting methylation scores wereused to distinguish the cancer samples from the normal samples. FIGS. 5and 6 show that the normalized coverage-weighted methylation scoredifference algorithm was able to detect 34 out of 36 CRC samples (94.4%sensitivity), 8 out of 11 BRCA samples (72.7% sensitivity), and 10 outof 19 lung cancer samples (52.6% sensitivity). The algorithm exhibited100% specificity, having correctly identified all 25 of the testingcontrol samples as normal.

Results obtained using the methods of this example provide highlysensitive and specific evaluation of the cumulative effect of multipleaffected CpG sites across the genome. By providing an alternative methodfor detecting methylation patterns, the methods of this example canprovide a more sensitive and specific cancer diagnosis than methodsrelying on detection of somatic mutations.

Example IV Clinical Performance of Cancer Type Classification MethodBased on Average Methylation Levels Across Preselected Subsets ofMethylation Sites

This example evaluates the clinical sensitivity of a method for cancertype classification based on average methylation levels acrosspreselected subsets of CpG methylation sites referred to herein as“hypermethylated” sites. The assay described in this example can beuseful for identifying the source of tumor in circulating cell-free DNA.

Correlation of Methylation Profiles Between Plasma and Tissue DNASamples

As an initial inquiry, we set out to determine how well the methylationprofiles of circulating tumor DNA (ctDNA) isolated from plasma samplescorrelated to those of DNA isolated from tumor tissues. A high degree ofcorrelation would lend credence to the idea that methylation profiles ofcfDNA can be used to classify the tumor of origin. To this end, wecompared the methylation profiles of the colorectal, breast and lungcancer samples that were detected in Example III to the averagemethylation profiles for each of the 32 cancer types from TCGA (TheCancer Genomic Atlas) that had a minimum of 30 cancer samples in thedatabase. The methylation profiles were determined substantially asdescribed in Examples I-III and consisted of methylation levels at 9,242CpG sites (poorly performing methylation sites from the original CpGpanels were filtered out to improve accuracy).

The comparison was performed in a pairwise manner between eachcancer-positive plasma sample from Example III and each of the 32 cancertype from TCGA, resulting in correlation coefficients ranging from 0to 1. The correlations were plotted as a two-dimensional correlationmap, which is shown in FIG. 7. The darker areas of the map correspond tohigher correlations, whereas the lighter areas of the map signify lowercorrelations. The observed correlations between the methylation profileswere generally highest for the matching tumor types. For example, in thebreast cancer samples from plasma, the correlation was highest to thebreast cancer tissue (breast invasive carcinoma), and lower in all othertumor tissue types. Similarly, for the CRC plasma samples, thecorrelation was highest to colon and rectum tissues (e.g., colonadenocarcinoma, esophageal carcinoma, rectum adenocarcinoma and stomachadenocarcinoma). The correlation was less pronounced in the lung cancersamples.

Development and Testing of Cancer Type Classification

Having determined that there is a significant correlation betweenmethylation profiles of ctDNA and DNA from tumor tissues, we proceededto develop and test a cancer type classification method in silico.

First, we identified 24 cancer types with more than 100 samples in theTCGA database. For each of these types, we created a list of“hypermethylated” sites, which were defined as sites having a meanmethylation level (across samples) in the top 3% across the entire paneland greater than 6% in terms of absolute values.

Given a test sample, we determined its cancer types in a three-stepprocess. First, for each of the 24 cancer types, the methylation levelsfor each of the “hypermethylated” sites on the list were determined asdescribed in Examples I-III. Next, the average methylation level acrossthe “hypermethylated” sites were calculated for each of the 24 cancertypes. Finally, each of the 24 cancer types was ranked by their averagemethylation levels across the “hypermethylated” sites and classified thetest sample by the cancer type with the highest average methylationlevel.

We then proceeded to back-test the method on each of the TCGA tissuesamples that was used to generate the lists of “hypermethylated” sites.Accuracy of the method was defined as the ratio of the number of cancersamples of a particular type that were identified correctly to the totalnumber of samples of that cancer type. Results of this analysis areshown in FIG. 8. As one can easily see from this figure, 22 out of 24cancer types were classified with over 75% accuracy. Indeed, many of thecancer types were correctly identified about 90% of the time or better.Only two types—esophageal carcinoma and testicular germ celltumors—failed to cross the 75% threshold.

Cancer Type Classification of Plasma Samples

The 52 plasma samples correctly identified as cancer samples in ExampleIII (34 CRC, 8 BRCA, and 10 lung) were subjected to the cancer typeclassification analysis as described above. Results of this analysis areshown in FIG. 9. The cancer classification algorithm correctlyidentified 28 out of 34 CRC samples (82%), 7 out of 8 BRCA samples (88%)and 7 out of 10 lung cancer samples (70%). These results demonstratethat the cancer type classification method described herein may be usedwith a high clinical sensitivity to identify the tissue of origin inctDNA from plasma samples.

Throughout this application various publications, patents or patentapplications have been referenced. The disclosures of these publicationsin their entireties are hereby incorporated by reference in thisapplication in order to more fully describe the state of the art towhich this invention pertains.

The term “comprising” is intended herein to be open-ended, including notonly the recited elements, but further encompassing any additionalelements.

Although the invention has been described with reference to the examplesprovided above, it should be understood that various modifications canbe made without departing from the invention. Accordingly, the inventionis limited only by the claims.

What is claimed is:
 1. A method for monitoring for relapse of a tumor inan individual treated for cancer, comprising (a) providing a cell freeDNA sample from plasma comprising a mixture of circulating tumor andnon-tumor DNA obtained from the individual at a first time point afterinitiation of cancer treatment, thereby providing test genomic DNA; (b)detecting methylation states for a plurality of CpG sites in the testgenomic DNA; (c) determining the coverage at each of the CpG sites forthe detecting of the methylation states; (d) providing methylationstates for the plurality of CpG sites in reference genomic DNA from atleast one reference individual; (e) determining, for each of the CpGsites, the methylation difference between the test genomic DNA and thereference genomic DNA, thereby providing a normalized methylationdifference for each CpG site; (f) weighting the normalized methylationdifference for each CpG site by the coverage at each of the CpG sites,thereby determining an aggregate coverage-weighted normalizedmethylation difference score; (g) repeating steps (a) through (f) usinga second test genomic DNA provided from plasma comprising a mixture ofcirculating tumor and non-tumor DNA obtained from the individual at asecond time point after initiation of cancer treatment, and using thesame reference genomic DNA from the at least one reference individual;(h) determining that a change has occurred in the aggregatecoverage-weighted normalized methylation difference score between thetest genomic DNA and the second test genomic DNA; wherein said change isindicative of relapse of said tumor in said individual, and (i) treatingthe individual with a therapy to reduce the relapse, wherein the therapycomprises surgery, chemotherapy, or radiation therapy.
 2. The method ofclaim 1, wherein the providing of the sample in step (a) comprisestargeted selection of a subset of genomic DNA fragments comprising a setof predetermined target CpG sites.
 3. The method of claim 2, wherein theproviding of the sample in step (a) further comprises treating thesubset of genomic DNA fragments with bisulfate.
 4. The method of claim1, wherein the detecting in step (b) comprises a sequencing techniquethat serially distinguishes nucleotides in the test genomic DNA.
 5. Themethod of claim 4, wherein the sequencing technique comprises massivelyparallel sequencing.
 6. The method of claim 1, wherein the normalizedmethylation difference at a particular CpG site is determined accordingto $Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$ wherein Z_(i)represents a normalized methylation difference for a particular CpG siteidentified as i, χ_(i) represents the methylation level at CPG site i inthe test genomic DNA, μ_(i) represents the mean methylation level at CpGsite i in the reference genome, and σ_(i), represents the standarddeviation of methylation levels at CpG site i in the reference genomicDNA.
 7. The method of claim 6, wherein the aggregate coverage-weightednormalized methylation difference score (represented as A) is determinedaccording to$A = \frac{\sum_{i = 1}^{k}\;{w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}\; w_{i}^{2}}}$wherein w_(i) represents the coverage at CpG site i, and k representsthe total number of CpG sites.
 8. The method of claim 1, wherein thetest sample is from an individual known or suspected to have cancer. 9.The method of claim 1, wherein the test organism is a human.
 10. Themethod of claim 9, wherein the method further comprises communicatingcounseling information to the human.
 11. The method of claim 1, whereinthe detecting in step (b) comprises: (b1) treating the test genomic DNAwith bisulfite to generate bisulfite treated DNA; (b2) enriching for asubset of fragments of the bisulfite treated DNA by targeted selection,the subset comprising a set of predetermined target CpG sites; (b3)performing massively parallel sequencing of the subset to generatesequence reads; and (b4) aligning the sequence reads to a reference. 12.The method of claim 11, wherein the subset comprises at least 100predetermined target CpG sites.
 13. The method of claim 11, wherein thesubset comprises at least 1000 predetermined target CpG sites.
 14. Themethod of claim 11, wherein the massively parallel sequencing generatesat least 10,000 sequence reads.